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In recent times, a new wave of scientific and technological advancements has significantly reshaped the global economic structure. This shift has redefined the role of regional innovation, particularly in its contribution to developing the Guangdong–Hong Kong–Macao Greater Bay area (GBA) into a renowned center for science, technology, and innovation. This study constructs a comprehensive evaluation system for the Regional Innovation Ecosystem (RIE). By applying the coupling coordination degree model and social network analysis, we have extensively analyzed the spatial structure and network attributes of the coupled and coordinated innovation ecosystem in the GBA from 2010 to 2019. Our findings reveal several key developments: (1) There has been a noticeable rightward shift in the kernel density curve, indicating an ongoing optimization of the overall coupling coordination level. Notably, the center of gravity for coupling coordination has progressively moved southeast. This shift has led to a reduction in the elliptical area each year, while the trend surface consistently shows a convex orientation toward the center. The most significant development is observed along the 'Guangdong–Shenzhen–Hong Kong–Macao Science and Technology Innovation Corridor', where the level of coupling coordination has become increasingly pronounced. (2) The spatial linkages within the GBA have been strengthening. There are significant spatial transaction costs in the regional innovation ecological network. In the context of the 2019 US-China trade war, the cities of Jiangmen and Zhaoqing experienced a notable decrease in connectivity with other cities, raising concerns about their potential marginalization. (3) Guangzhou, Shenzhen, and Hong Kong have emerged as core nodes within the network. The network exhibits a distinctive "core–edge" spatial structure, characterized by both robustness and vulnerability in various aspects. [ABSTRACT FROM AUTHOR]

Healthcare disparities in multiethnic medical data is a major challenge; the main reason lies in the unequal data distribution of ethnic groups among data cohorts. Biomedical data collected from different cancer genome research projects may consist of mainly one ethnic group, such as people with European ancestry. In contrast, the data distribution of other ethnic races such as African, Asian, Hispanic, and Native Americans can be less visible than the counterpart. Data inequality in the biomedical field is an important research problem, resulting in the diverse performance of machine learning models while creating healthcare disparities. Previous researches have reduced the healthcare disparities only using limited data distributions. In our study, we work on fine-tuning of deep learning and transfer learning models with different multiethnic data distributions for the prognosis of 33 cancer types. In previous studies, to reduce the healthcare disparities, only a single ethnic cohort was used as the target domain with one major source domain. In contrast, we focused on multiple ethnic cohorts as the target domain in transfer learning using the TCGA and MMRF CoMMpass study datasets. After performance comparison for experiments with new data distributions, our proposed model shows promising performance for transfer learning schemes compared to the baseline approach for old and new data distributation experiments. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM. [ABSTRACT FROM AUTHOR]

Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Method A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK) for cancer detection. The results reveal that our proposed method (i.e. CancerEMC) can achieve the best performance result for both binary cancer classification with 99.17% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.12% accuracy (AUC = 0.938). Addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.50% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it brings opportunities to understand the underlying processes in a high-throughput manner. Deep embedded clustering (DEC) was demonstrated successful in high-dimensional sparse scRNA-seq data by joint feature learning and cluster assignment for identifying cell types simultaneously. However, the deep network architecture for embedding clustering is not trivial to optimize. Therefore, we propose an evolutionary multiobjective DEC by synergizing the multiobjective evolutionary optimization to simultaneously evolve the hyperparameters and architectures of DEC in an automatic manner. Firstly, a denoising autoencoder is integrated into the DEC to project the high-dimensional sparse scRNA-seq data into a low-dimensional space. After that, to guide the evolution, three objective functions are formulated to balance the model's generality and clustering performance for robustness. Meanwhile, migration and mutation operators are proposed to optimize the objective functions to select the suitable hyperparameters and architectures of DEC in the multiobjective framework. Multiple comparison analyses are conducted on twenty synthetic data and eight real data from different representative single-cell sequencing platforms to validate the effectiveness. The experimental results reveal that the proposed algorithm outperforms other state-of-the-art clustering methods under different metrics. Meanwhile, marker genes identification, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cell type identification and characterization mechanisms. [ABSTRACT FROM AUTHOR]

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1. [ABSTRACT FROM AUTHOR]

Motivation In recent years, single-cell RNA sequencing enables us to discover cell types or even subtypes. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability. Results We propose an evolutionary multiobjective ensemble pruning algorithm (EMEP) that addresses those realistic restrictions. Our EMEP algorithm first applies the unsupervised dimensionality reduction to project data from the original high dimensions to low-dimensional subspaces; basic clustering algorithms are applied in those new subspaces to generate different clustering results to form cluster ensembles. However, most of those cluster ensembles are unnecessarily bulky with the expense of extra time costs and memory consumption. To overcome that problem, EMEP is designed to dynamically select the suitable clustering results from the ensembles. Moreover, to guide the multiobjective ensemble evolution, three cluster validity indices including the overall cluster deviation, the within-cluster compactness and the number of basic partition clusters are formulated as the objective functions to unleash its cell type discovery performance using evolutionary multiobjective optimization. We applied EMEP to 55 simulated datasets and seven real single-cell RNA-seq datasets, including six single-cell RNA-seq dataset and one large-scale dataset with 3005 cells and 4412 genes. Two case studies are also conducted to reveal mechanistic insights into the biological relevance of EMEP. We found that EMEP can achieve superior performance over the other clustering algorithms, demonstrating that EMEP can identify cell populations clearly. Availability and implementation EMEP is written in Matlab and available at https://github.com/lixt314/EMEP Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The single batch-processing machine problem is to minimize makespan, which has broad applications, including engineering fundamentals and theoretical background. The machine can process several jobs as a batch simultaneously, and every job has three different attributes: job size, processing time, and job arrival. In this paper, a hybrid local search algorithm with path relinking (LP) is devised to solve the problem. We not only generate an optimal initial solution firstly but also pay more attention to improving the solution quality. Three kinds of local searches are applied to enhance the diversity of solutions; the path relinking is adopted to explore better solutions through local tracks connecting the current solution and the best in the elite set. With these approaches, it can keep a balanced rate between exploratory and exploitative capacity. Computational experiments on 40 benchmark instances indicate that LP has the superior convergence and robust performance and it surpasses the current state-of-the-art methods such as genetic algorithm and ant colony optimization, especially for large instances. [ABSTRACT FROM AUTHOR]

The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D, to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator. [ABSTRACT FROM AUTHOR]

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective. [ABSTRACT FROM AUTHOR]

This paper focuses on the multiobjective solutions of the flow shop scheduling problem with and without sequence dependent setup times. In our case, the two objectives are the minimization of makespan and total flowtime. These problems are solved with a novel multiobjective local search framework-based decomposition, called multiobjective local search based decomposition (MOLSD), which decomposes a multiobjective problem into a number of single objective optimization subproblems using aggregation method and optimizes them simultaneously. First, a problem-specific Nawaz–Enscore–Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a Pareto local search embedded with a heavy perturbation operator is applied to search the promising neighbors of each nondominated solution found so far. Then, when solving each subproblem, a single insert-based local search, a multiple local search strategy, and a doubling perturbation mechanism are designed to exploit the new individual. Finally, a restarted method is used to avoid the algorithm trapping into the local optima, which has a significant effect on the performance of the MOLSD. Comprehensive experiments have been conducted by two standard multiobjective metrics: 1) hyper-volume indicator; and 2) set coverage. The experimental results show that the proposed MOLSD provides better solutions that several state of the art algorithms. [ABSTRACT FROM PUBLISHER]

Venous cystic adventitial disease (VCAD) is a rare vascular anomaly located in the common femoral vein in most cases. We describe the case of a 59-year-old female patient with right leg edema who was misdiagnosed with deep vein thrombosis of the lower extremity at another hospital. Magnetic resonance angiography revealed a round mass in the popliteal vein, with a narrow lumen. Considering the location of the lesion, absence of a history of deep venous thrombosis and trauma, and clinical manifestations, the diagnosis is likely a popliteal vein adventitial cyst. Segmental popliteal vein resection and reconstruction were performed using a cylindrical great saphenous vein graft. No joint connection was found during the operation, and the postoperative pathology confirmed VCAD. [ABSTRACT FROM AUTHOR]

In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained. [ABSTRACT FROM AUTHOR]

The cuckoo search algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, we use a new search strategy based on orthogonal learning strategy to enhance the exploitation ability of the basic cuckoo search algorithm. In order to verify the performance of our approach, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. [ABSTRACT FROM AUTHOR]

The artificial bee colony is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem, and later, it was extended to constrained design problems as well. This paper describes a self-adaptive constrained artificial bee colony algorithm for constrained optimization problem based on feasible rule method and multiobjective optimization method. The employed bee colony severs as the global search engine for each population based on feasible rule. Then, the onlooker bee colony can explore the new search space based on the multiobjective optimization. In order to enhance the convergence rate of the proposed algorithm, a self-adaptive modification rate is proposed to make the algorithm can change many parameters. To verify the performance of our approach, 24 well-known constrained problems from 2006 IEEE congress on Evolution Computation (CEC2006) are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, state-of-the-art approaches in terms of the quality of the resulting solutions from literature. [ABSTRACT FROM AUTHOR]

Abstract: The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a differential evolution (DE) based memetic algorithm, named ODDE, is proposed for PFSSP. First, to make DE suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in DE to the discrete job permutation. Second, the NEH heuristic was combined the random initialization to the population with certain quality and diversity. Third, to improve the global optimization property of DE, a DE approach based on measure of population’s diversity is proposed to tuning the crossover rate. Fourth, to improve the convergence rate of DE, the opposition-based DE employs opposition-based learning for the initialization and for generation jumping to enhance the global optimal solution. Fifth, the fast local search is used for enhancing the individuals with a certain probability. Sixth, the pairwise based local search is used to enhance the global optimal solution and help the algorithm to escape from local minimum. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, which show that our algorithm is both effective and efficient. We have also evaluated our algorithm with the well known DMU problems. For the problems with the objective of minimizing makespan, the algorithm ODDE obtains 24 new upper bounds of the 40 instances, and for the problems with the objective of maximum lateness, ODDE obtains 137 new upper bounds of the 160 instances. These new upper bounds can be used for future algorithms to compare their results with ours. [Copyright &y& Elsevier]

Abstract: Biogeography based optimization (BBO) is a new evolutionary optimization based on the science of biogeography for global optimization. We propose two extensions to BBO. First, we propose a new migration operation based multi-parent crossover called multi-parent migration model, which is a generalization of the standard BBO migration operator. The new migration model can satisfy a balance of exploration and exploitation. Second, the Gaussian mutation operator is integrated into multi-operator biogeography based optimization (MOBBO) to enhance its exploration ability and to improve the diversity of population. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed MOBBO algorithm based multi-parent crossover model is better, or at least comparable to, the BBO, PBBO and evolutionary algorithms from literature when considering the quality of the solutions obtained. [Copyright &y& Elsevier]

The reconfigurable design problem is to find the element that will result in a sector pattern main beam with side lobes. The same excitation amplitudes applied to the array with zero phase should be in a high directivity, low-side lobe pencil-shaped main beam. This work presents a multiobjective approach to solve this problem. We consider two design objectives: the minimum value for the dual beam and the dynamic range ratio in qualify the entire array radiation pattern in order to achieve the optimal value between the antenna-array elements. We use a recently developed and very competitive multiobjective evolutionary algorithm, called MOEA/D. This algorithm uses a decomposition approach to convert the problem of approximation of the Pareto Front into a number of single objective optimization problems. We illustrate that the best solutions obtained by the MOEA/D can outperform stat-of-art single objective algorithm: generalized generation-gap model genetic algorithm (G3-GA) and differential evolution algorithm (DE). In addition, we compare the results obtained by MOEA/D with those obtained by one of the most widely multiobjective algorithm called NSGA-II and mutliobjective DE. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 675-681, 2012. [ABSTRACT FROM AUTHOR]

Abstract: Biogeography based optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. We propose three extensions to BBO. First, we propose a new migration operation based sinusoidal migration model called perturb migration, which is a generalization of the standard BBO migration operator. Then, the Gaussian mutation operator is integrated into perturb biogeography based optimization (PBBO) to enhance its exploration ability and to improve the diversity of population. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed PBBO algorithm using sinusoidal migration model is better, or at least comparable to, the RCBBO based linear model, RCBBO-G, RCBBO–L and evolutionary algorithms from literature when considering the quality of the solutions obtained. [Copyright &y& Elsevier]

Postmenopausal osteoporosis (PMOP) is a common kind of osteoporosis that is associated with excessive osteocyte death and bone loss. Previous studies have shown that TNF-α-induced osteocyte necroptosis might exert a stronger effect on PMOP than apoptosis, and TLR4 can also induce cell necroptosis, as confirmed by recent studies. However, little is known about the relationship between TNF-α-induced osteocyte necroptosis and TLR4. In the present study, we showed that TNF-α increased the expression of TLR4, which promoted osteocyte necroptosis in PMOP. In patients with PMOP, TLR4 was highly expressed at skeletal sites where exists osteocyte necroptosis, and high TLR4 expression is correlated with enhanced TNF-α expression. Osteocytes exhibited robust TLR4 expression upon exposure to necroptotic osteocytes in vivo and in vitro. Western blotting and immunofluorescence analyses demonstrated that TNF-α upregulated TLR4 expression in vitro, which might further promote osteocyte necroptosis. Furthermore, inhibition of TLR4 by TAK-242 in vitro effectively blocked osteocyte necroptosis induced by TNF-α. Collectively, these results suggest a novel TLR4-mediated process of osteocyte necroptosis, which might increase osteocyte death and bone loss in the process of PMOP. • TNF-α promote osteocytes necroptosis through upregulated TLR4 in postmenopausal osteoporosis. • High TLR4 expression is related to increased expression of TNF-α. • TLR4 is upregulated in necroptotic osteocyte, and inhibition of osteocytes necroptosis reduces the expression of TLR4. • Inhibition of TLR4 block TNF-α-induced osteocytes necroptosis in vitro. [ABSTRACT FROM AUTHOR]

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452. We implement SOFB, an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method to characterize protein sequences by learning the semantics of biological dynamics contexts. [ABSTRACT FROM AUTHOR]

Motivation The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules. Results We evaluated the classification part on 'DDIs 2013' dataset and 'DTIs' dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships. Availability Source code are publicly available at https://github.com/Layne-Huang/EGFI. [ABSTRACT FROM AUTHOR]

Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution‐agnostic deep learning model for accurately recovering missing sing‐cell gene expression from multiple platforms. Bis is an optimal transport‐based autoencoder model that can capture the intricate distribution of scRNA‐seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA‐seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor‐matched peripheral blood dataset, revealing developmental characteristics of cytokine‐induced natural killer cells within a head and neck squamous cell carcinoma microenvironment. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to incomplete genome annotation and an emphasis on known miRNA-mRNA interactions, restricting predictions of unknown ones. To address those challenges, we have developed a deep learning model based on miRNA functional target identification, named miTDS, to investigate miRNA–mRNA interactions. miTDS first employs a scoring mechanism to eliminate unstable sequence pairs and then utilizes a dynamic word embedding model based on the transformer architecture, enabling a comprehensive analysis of miRNA-mRNA interaction sites by harnessing the global contextual associations of each nucleotide. On this basis, miTDS fuses extended seed alignment representations learned in the multi-scale attention mechanism module with dynamic semantic representations extracted in the RNA-based dual-path module, which can further elucidate and predict miRNA and mRNA functions and interactions. To validate the effectiveness of miTDS, we conducted a thorough comparison with state-of-the-art miRNA-mRNA functional target prediction methods. The evaluation, performed on a dataset cross-referenced with entries from MirTarbase and Diana-TarBase, revealed that miTDS surpasses current methods in accurately predicting functional targets. In addition, our model exhibited proficiency in identifying A-to-I RNA editing sites, which represents an aberrant interaction that yields valuable insights into the suppression of cancerous processes. • Utilizes Bidirectional Encoder Representations from Transformers to explore local nucleotide dependencies. • Inception-Attention Hybrid Network processes seed alignments, capturing multi-scale features effectively. • Combines CNN and BiLSTM for integrating dynamic semantics, enhancing pattern recognition in sequences. [ABSTRACT FROM AUTHOR]

Single‐cell Hi‐C (scHi‐C) has made it possible to analyze chromatin organization at the single‐cell level. However, scHi‐C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single‐cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi‐C loop calling by adapting the training and inferencing strategies of graph‐based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single‐cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi‐connected hubs and their underlying mechanisms. [ABSTRACT FROM AUTHOR]

Motivation Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. Results To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. Availability and implementation The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001. [ABSTRACT FROM AUTHOR]

Unsupervised clustering is an essential step in identifying cell types from single‐cell RNA sequencing (scRNA‐seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single‐cell molecular heterogeneity. In particular, a silhouette coefficient‐based indicator is developed to determine the optimization direction of the bi‐objective function. In addition, a hierarchical autoencoder is employed to project the high‐dimensional data onto multiple low‐dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi‐objective fruit fly optimization algorithm is designed to prune dynamically the low‐quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA‐seq datasets and one large real scRNA‐seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post‐transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms. [ABSTRACT FROM AUTHOR]

Motivation The RNA-guided CRISPR/Cas9 system has been widely applied to genome editing. CRISPR/Cas9 system can effectively edit the on-target genes. Nonetheless, it has recently been demonstrated that many homologous off-target genomic sequences could be mutated, leading to unexpected gene-editing outcomes. Therefore, a plethora of tools were proposed for the prediction of off-target activities of CRISPR/Cas9. Nonetheless, each computational tool has its own advantages and drawbacks under diverse conditions. It is hardly believed that a single tool is optimal for all conditions. Hence, we would like to explore the ensemble learning potential on synergizing multiple tools with genomic annotations together to enhance its predictive abilities. Results We proposed an ensemble learning framework which synergizes multiple tools together to predict the off-target activities of CRISPR/Cas9 in different combinations. Interestingly, the ensemble learning using AdaBoost outperformed other individual off-target predictive tools. We also investigated the effect of evolutionary conservation (PhyloP and PhastCons) and chromatin annotations (ChromHMM and Segway) and found that only PhyloP can enhance the predictive capabilities further. Case studies are conducted to reveal ensemble insights into the off-target predictions, demonstrating how the current study can be applied in different genomic contexts. The best prediction predicted by AdaBoost is up to 0.9383 (AUC) and 0.2998 (PRC) that outperforms other classifiers. This is ascribable to the fact that AdaBoost introduces a new weak classifier (i.e. decision stump) in each iteration to learn the DNA sequences that were misclassified as off-targets until a small error rate is reached iteratively. Availability and implementation The source codes are freely available on GitHub at https://github.com/Alexzsx/CRISPR. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization. [ABSTRACT FROM AUTHOR]

The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat‐resistant while Gefitinib‐sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. Results To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. Availability and implementation The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/. An ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI) accurately predicts PPIs through protein language models and outperforms state-of-the-art methods on multiple benchmark sets. [ABSTRACT FROM AUTHOR]

Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene's transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning–based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework. Author summary: Numerous evidence suggest that genes regulated by enhancers located in non-coding DNA regions are involved in a myriad of biological activities. To fully understand the regulatory role and mechanisms of enhancers on genes, the localization and identification of enhancers is essential. Several experimental biological methods are capable of localizing enhancers, however, these methods are resource intensive. To address this limitation, we developed a stacked multivariate fusion framework, called SMFM to identify and analyze enhancers with high accuracy and efficiency based on enhancer-specific dynamic semantic information and multi-source biological properties. The performance of the model is verified by experiments comparing different feature algorithms and classification algorithms. The superiority of our method is demonstrated by comparing it with several state-of-the-art algorithms. In addition, several analytical experiments demonstrate that SMFM is capable of recognizing enhancers in different tissues and detecting motifs in enhancers. To the best of our knowledge, this is the first computational approach that uses enhancer-specific dynamic semantic information to identify enhancers from regulatory DNA sequences and interpret them. It is expected that the SMFM model will effectively target enhancers and provide valid candidates for further biological experiments. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. Results In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. Availability and implementation The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Objective: Long non-coding RNA (lncRNA) essentially controls many physiological and pathological processes of deep vein thrombosis (DVT). Based on that, lncRNA taurine upregulated gene 1 (TUG1)-involved angiogenesis of endothelial progenitor cells (EPCs) and dissolution of DVT was explored. Methods: In the in-vitro experiments, EPCs were engineered with mimic, inhibitor, siRNA, and plasmid, after which tube formation, proliferation, migration, and apoptosis were checked. In the in-vivo experiments, a DVT mouse model was established. Before the DVT operation, the mice were injected with agomir, antagomir, siRNA, and plasmid. Subsequently, thrombosis and damage to the femoral vein were pathologically evaluated. TUG1, miR-92a-3p, and 3-Hydroxy-3-methylglutaryl coenzyme A reductase (Hmgcr) expression in the femoral vein was tested. The relationship between TUG1, miR-92a-3p, and Hmgcr was validated. Results: DVT mice showed suppressed TUG1 and Hmgcr expression, and elevated miR-92a-3p expression. In EPCs, TUG1 overexpression or miR-92a-3p inhibition promoted cellular angiogenesis, whereas Hmgcr silencing blocked cellular angiogenesis. In DVT mice, elevated TUG1 or inhibited miR-92a-3p suppressed thrombosis and damage to the femoral vein whilst Hmgcr knockdown acted oppositely. In both cellular and animal models, TUG1 overexpression-induced effects could be mitigated by miR-92a-3p up-regulation. Mechanically, TUG1 interacted with miR-92a-3p to regulate Hmgcr expression. Conclusion: Evidently, TUG1 promotes the angiogenesis of EPCs and dissolution of DVT via the interplay with miR-92a-3p and Hmgcr. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC. [ABSTRACT FROM AUTHOR]

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths. [ABSTRACT FROM AUTHOR]

Motivation Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. Results In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. Availability and implementation The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency. Methods: To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, L1 and L2 regularization and dropout layers are added. Results: For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms. Conclusions: DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity. [ABSTRACT FROM AUTHOR]

Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms within circRNAs. Thanks to the development of cross-linked immunoprecipitation sequencing technology, large amounts of genome-wide circRNA binding event data have accumulated, providing opportunities for designing high-performance computational models to discriminate RBP interaction sites and thus to interpret the biological significance of circRNAs. Unfortunately, there are still no computational models sufficiently flexible to accommodate circRNAs from different data scales and with various degrees of feature representation. Here, we present HCRNet, a novel end-to-end framework for identification of circRNA-RBP binding events. To capture the hierarchical relationships, the multi-source biological information is fused to represent circRNAs, including various natural language sequence features. Furthermore, a deep temporal convolutional network incorporating global expectation pooling was developed to exploit the latent nucleotide dependencies in an exhaustive manner. We benchmarked HCRNet on 37 circRNA datasets and 31 linear RNA datasets to demonstrate the effectiveness of our proposed method. To evaluate further the model's robustness, we performed HCRNet on a full-length dataset containing 740 circRNAs. Results indicate that HCRNet generally outperforms existing methods. In addition, motif analyses were conducted to exhibit the interpretability of HCRNet on circRNAs. All supporting source code and data can be downloaded from https://github.com/yangyn533/HCRNet and https://doi.org/10.6084/m9.figshare.16943722.v1. And the web server of HCRNet is publicly accessible at http://39.104.118.143:5001/. [ABSTRACT FROM AUTHOR]

Motivation RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance. Results Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein–RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives. Availability and implementation The EDCNN algorithm is available at GitHub: https://github.com/yaweiwang1232/EDCNN. Both the software and the supporting data can be downloaded from: https://figshare.com/articles/software/EDCNN/16803217. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Class-imbalanced biological datasets pose significant challenges in machine learning and data analysis tasks. Prior methods to handle imbalance rely on data oversampling, which increases computational costs and overfitting. While feature selection and ensemble learning are promising techniques, current applications in imbalanced contexts are limited. To address these challenges, we present a novel framework called Hybrid Sampling Nature-Inspired Optimization Ensemble (HSNOE) to enhance the identification of hidden responders in imbalanced biological datasets. Our contributions are three-fold: 1) A hybrid undersampling and oversampling technique to mitigate class-imbalance; 2) Integrate an ant colony optimization-based feature selection that identifies informative feature subsets; 3) An ensemble classifier integrating diverse models trained on optimized features to improve performance. The experiments conducted on the five biological datasets demonstrate that HSNOE exhibits more stable comprehensive performance across six evaluation metrics compared to ten benchmark methods. We also conducted a biological analysis specifically on the Pan-cancer dataset. Moreover, the HSNOE method has been made publicly available on GitHub. 1 1 https://github.com/wangxb96/HSNOE. • HSNOE can mitigate the effects of data skew when training the model. • A nature-inspired feature selection method is used to identify informative features. • HSNOE combines a nature-inspired optimization method to realize ensemble. • A case study was conducted to show the generalization capabilities of HSNOE. [ABSTRACT FROM AUTHOR]

Mitochondria are membrane-bound organelles containing over 1000 different proteins involved in mitochondrial function, gene expression and metabolic processes. Accurate localization of those proteins in the mitochondrial compartments is critical to their operation. A few computational methods have been developed for predicting submitochondrial localization from the protein sequences. Unfortunately, most of these computational methods focus on employing biological features or evolutionary information to extract sequence features, which greatly limits the performance of subsequent identification. Moreover, the efficiency of most computational models is still under explored, especially the deep learning feature, which is promising but requires improvement. To address these limitations, we propose a novel computational method called iDeepSubMito to predict the location of mitochondrial proteins to the submitochondrial compartments. First, we adopted a coding scheme using the ProteinELMo to model the probability distribution over the protein sequences and then represent the protein sequences as continuous vectors. Then, we proposed and implemented convolutional neural network architecture based on the bidirectional LSTM with self-attention mechanism, to effectively explore the contextual information and protein sequence semantic features. To demonstrate the effectiveness of our proposed iDeepSubMito, we performed cross-validation on two datasets containing 424 proteins and 570 proteins respectively, and consisting of four different mitochondrial compartments (matrix, inner membrane, outer membrane and intermembrane regions). Experimental results revealed that our method outperformed other computational methods. In addition, we tested iDeepSubMito on the M187, M983 and MitoCarta3.0 to further verify the efficiency of our method. Finally, the motif analysis and the interpretability analysis were conducted to reveal novel insights into subcellular biological functions of mitochondrial proteins. iDeepSubMito source code is available on GitHub at https://github.com/houzl3416/iDeepSubMito. [ABSTRACT FROM AUTHOR]

This paper further explores the phase transitions of the EXPSPACE-complete problems, focusing on the conformant plan modification. By analyzing features of the conformant plan modification, quantitative results are obtained. If the number of actions is't greater than θub, almost all the conformant planning instances can't be solved with the conformant plan modification. If the number of actions is't lower than θlb, almost all the conformant planning instances can be solved with the conformant plan modification. The results of the experiments show that there indeed exists an experimental threshold γc of density (ratio of number of actions to number of propositions), which separates the region where almost all conformant planning instances can't be solved with the conformant plan modification from the region where almost all conformant planning instances can be solved with the conformant plan modification. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problems with fuzzy processing time and fuzzy due date are investigated in this paper. The ranking concept among fuzzy numbers based on possibility and necessity measures which are developed in fuzzy sets theory is introduced. And on the basis of two consistent measures in this concept, several novel objective functions are proposed. The purpose of our research is to obtain the optimal schedules based on these objective functions. A modified DE algorithm will be designed to solve these objective functions. Several jop-shop scheduling problems with fuzzy processing time and fuzzy due date are experimented to show the efficiency and comparability of our approach. Through the experimental results, the potential application of the possibility and necessity theory in the real world is shown. [ABSTRACT FROM AUTHOR]