Search

Motivation Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges. Results In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. Availability and implementation https://github.com/Teddy-XiongGZ/DeepGSEA [ABSTRACT FROM AUTHOR]

Fruit color is an intuitive quality of horticultural crops that can be used as an evaluation criterion for fruit ripening and is an important factor affecting consumers' purchase choices. In this study, a genetic population from the cross of green peel 'Qidong' and purple peel '8 guo' revealed that the purple to green color of eggplant peel is dominant and controlled by a pair of alleles. Bulked segregant analysis (BSA), SNP haplotyping, and fine genetic mapping delimited candidate genes to a 350 kb region of eggplant chromosome 10 flanked by markers KA2381 and CA8828. One ANS gene (EGP22363) was predicted to be a candidate gene based on gene annotation and sequence alignment of the 350-kb region. Sequence analysis revealed that a single base mutation of 'T' to 'C' on the exon green peel, which caused hydrophobicity to become hydrophilic serine, led to a change in the three-level spatial structure. Additionally, EGP22363 was more highly expressed in purple peels than in green peels. Collectively, EGP22363 is a strong candidate gene for anthocyanin biosynthesis in purple eggplant peels. These results provide important information for molecular marker-assisted selection in eggplants, and a basis for analyzing the regulatory pathways responsible for anthocyanin biosynthesis in eggplants. [ABSTRACT FROM AUTHOR]

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval. [ABSTRACT FROM AUTHOR]

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the presence of long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here, we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the model's output in terms of the contributions of its input features. The method is used for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDN's ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identify potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data. [ABSTRACT FROM AUTHOR]

BACKGROUND: The enzymes involved in the 2‐C‐methyl‐d‐erythritol 4‐phosphate (MEP) pathway are attractive targets of a new mode of action for developing anti‐infective drugs and herbicides, and inhibitors against 1‐deoxy‐d‐xylulose 5‐phosphate reductoisomerase (IspC), the second key enzyme in the pathway, have been intensively investigated; however, few works are reported regarding IspC inhibitors designed for new herbicide discovery. RESULTS: A series of fosmidomycin (FOS) analogs were designed with nitrogen‐containing linkers replacing the trimethylene linker between the two active substructures of FOS, phosphonic acid and hydroxamic acid. Synthesis followed a facile three‐step route of sequential aza‐Michael addition of α‐amino acids to dibenzyl vinylphosphonate, amidation of the amino acid carboxyl with O‐benzyl hydroxylamine, and simultaneous removal of the benzyl protective groups. Biological activity evaluation of IspC and model plants revealed that some compounds had moderate enzyme and model plant growth inhibition effects. In particular, compound 10g, which has a N‐(4‐fluorophenylethyl) nitrogen‐containing linker, exhibited the best plant inhibition activities, superior to the control FOS against the model plants Arabidopsis thaliana, Brassica napus L., Amaranthus retroflexus and Echinochloa crus‐galli. A dimethylallyl pyrophosphate rescue assay on A. thaliana confirmed that both 10g and FOS exert their herbicidal activity by blocking the MEP pathway. This result consistent with molecular docking, which confirmed 10g and FOS binding to the IspC active site in a similar way. CONCLUSION: Compound 10g has excellent herbicidal activity and represents the first herbicide lead structure of a new mode of action that targets IspC enzyme in the MEP pathway. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Fosmidomycin (FOS) is a naturally occurring compound active against the 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) enzyme in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, and using it as a template for lead structure design is an effective strategy to develop new active compounds. In this work, by replacing the hydroxamate unit of FOS with pyrazole, isoxazole and the related heterocycles that also have metal ion binding affinity, while retaining the monophosphonic acid in FOS or replacing it with a bisphosphonic acid group, heterocycle-containing mono- and bisphosphonic acid compounds as FOS analogs were designed. The key steps involved in the facile synthesis of these FOS analogs included the Michael addition of diethyl vinylphosphonate or tetraethyl vinylidenebisphosphonate to β-dicarbonyl compounds and the subsequent cyclic condensation with hydrazine or hydroxylamine. Two additional isoxazolinone-bearing FOS analogs were synthesized via the Michaelis–Becker reaction with diethyl phosphite as a key step. The bioactivity evaluation on model plants demonstrated that several compounds have better herbicidal activities compared to FOS, with the most active compound showing a 3.7-fold inhibitory activity on Arabidopsis thaliana, while on the roots and stalks of Brassica napus L. and Echinochloa crus-galli in a pre-emergence inhibitory activity test, the activities of this compound were found to be 3.2- and 14.3-fold and 5.4- and 9.4-fold, respectively, and in a post-emergency activity test on Amaranthus retroflexus and Echinochloa crus-galli, 2.2- and 2.0-fold inhibition activities were displayed. Despite the significant herbicidal activity, this compound exhibited a DXR inhibitory activity lower than that of FOS but comparable to that of other non-hydroxamate DXR inhibitors, and the dimethylallyl pyrophosphate rescue assay gave no statistical significance, suggesting that a different target might be involved in the inhibiting process. This work demonstrates that using bioisosteric replacement can be considered as a valuable strategy to discover new FOS analogs that may have high herbicidal activities. [ABSTRACT FROM AUTHOR]

As the essential component(s), long-chain perfluorinated or short-chain perfluorinated ionic surfactants are required for effective aqueous film-forming foam (AFFF); nevertheless, the associated qualities of persistent pollution and toxicity have raised significant concerns. It has become critical to develop alternatives to the present fluorine component for AFFF to offset the negative effects. In this study, a short-chain perfluorinated nitrogen-heterocyclic nonionic amine oxide surfactant was combined with hydrocarbon surfactants and additives to prepare an AFFF concentrate. A laboratory technique was developed to evaluate the influence of ingredients on the performance of a 6% AFFF diluent, resulting in an improved AFFF formulation. The performance parameters for pool fire extinguishment and fire resistance of the AFFF formulation were encouraging, including a spreading coefficient of 5.4, foam expansion of 8.11, 25% drainage time of 4.6 min, extinguishing times for forceful application of 58 s, and fire burnback time of 18.6 min. In addition, the AFFF concentrate showed significant freezing resistance when stored at − 20 °C for an extended period of time. The formulation outperformed the technical standard criteria and has the potential to be used as a novel AFFF agent. [ABSTRACT FROM AUTHOR]

Motivation The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells—some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them. Results We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective. Availability and implementation https://github.com/Teddy-XiongGZ/ProtoCell4P. [ABSTRACT FROM AUTHOR]

Fosmidomycin and its acetyl analogue FR900098 are two phosphorus-containing natural products with inhibitory activity against IspC enzyme in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway of isoprenoid biosynthesis in plants and most bacteria. This work presents a facile route for the chemical synthesis of fosmidomycin and FR900098 using readily available raw materials. Through optimizing reaction conditions of the key steps of Michaelis-Becker reaction and acylation, the monosodium salts of fosmidomycin and FR900098 were obtained in yields of 60% and 66%, respectively, over six steps in the route. This unified route provides an alternative to the reported methods and makes it possible to synthesize the two valuable compounds with scalability and low cost, having the potential for application in developing new antimalarial drugs and pesticides. [ABSTRACT FROM AUTHOR]

Motivation: Despite the success of recent machine learning algorithms' applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We use meta-learning, a machine-learning algorithm that is trained on a variety of related datasets and allows quick adaptations to new tasks, to perform survival analysis and pathway enrichment on pan-cancer datasets. In recent machine learning research, meta-learning has been effectively used for knowledge transfer among multiple related datasets. Results: We use meta-learning with Cox hazard loss to show that the integration of TCGA pan-cancer data increases the performance of survival analysis. We also apply advanced model interpretability method called DeepLIFT (Deep Learning Important FeaTures) to show different sets of enriched pathways for multi-omics and transcriptomics data. Our results show that multi-omics cancer survival analysis enhances performance compared with using transcriptomics or clinical data alone. Additionally, we show a correlation between variable importance assignment from DeepLIFT and gene coenrichment, suggesting that genes with higher and similar contribution scores are more likely to be enriched together in the same enrichment sets. Availability and implementation: https://github.com/berkuva/TCGA-omics-integration. [ABSTRACT FROM AUTHOR]

This paper presents the reflections of one of the authors, a junior faculty member, on teaching a master's level, English-medium instruction (EMI) course. The course was titled, International Research on Teacher Education, and it took place in a Chinese research-intensive university. It was, in part, a response to the need for internationalisation in higher education. The authors explore seven contradictions embedded in the EMI course: (1) teacher-centred vs. student-centred educational beliefs, (2) authoritarian vs. democratic classroom atmosphere, (3) traditional printed materials vs. multi-modal learning resources, (4) direct instruction vs. self-regulated learning, (5) classroom-based teaching vs. place-based learning, (6) individual learning vs. group-based work, and (7) summative assessment vs. formative assessment. The authors apply Engeström's cultural historical activity theory to extrapolate these contradictions. Using student narratives to demonstrate, the titular question of whether the course was a bane or a boon is answered. An autoethnographic or three-dimensional narrative approach was employed to analyse the data in the context of temporality, sociality, and place. Additionally, the authors discuss the dilemmatic space and implications associated with implementing the EMI course in China. [ABSTRACT FROM AUTHOR]

Browning of fresh-cut eggplant (Solanum melongena L.) reduces its sensory and nutritional qualities and further influences consumption. Polyphenolic oxidases (PPOs) are key enzymes involved in browning, but the mechanisms that regulate the expression of PPO genes are still unclear. Here, 12 SmPPO genes were identified and phylogenetic analysis clustered these genes into four branches. Protein and cis-regulatory element analyses showed that the SmPPO gene family has a conserved gene structure and diverse functions. Gene expression analysis in different tissues showed that the expression of SmPPO2, SmPPO3, SmPPO6, SmPPO7, and SmPPO10 was higher in the flesh of the browning-sensitive inbred line '36' than in the flesh of the browning-resistant line 'Fu'. Furthermore, almost all SmPPO genes in '36' were upregulated at 4 °C and 36 °C compared with those in 'Fu', and the expression increased earlier after harvest. In addition, SmPPO1, SmPPO6, SmPPO7, and SmPPO10 expression was significantly elevated in '36' after 2 days at 36 °C. These results suggest that SmPPOs are key modulators of eggplant browning and provide candidate genes for further research on the mechanisms regulating fruit browning. [ABSTRACT FROM AUTHOR]

Fruit brightness is an important quality trait that affects the market value of eggplant. However, few studies have been conducted on eggplant brightness. In this study, we aimed to identify genes related to this trait in three varieties of eggplant with different fruit brightness between 14 and 22 days after pollination. Using RNA-Seq Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, we found that wax- and cutin-related pathways and differentially expressed genes displayed significant differences among different development stages and varieties. Scanning electron microscopy revealed that the wax layer was thinner in '30-1' and 'QPCQ' than in '22-1'. Gas chromatography-mass spectrometry analysis revealed that wax content was significantly lower in '30-1' than in '22-1', which indicated that wax may be an important factor determining fruit brightness. We further identified and analyzed the KCS gene family, which encodes the rate-limiting enzyme of FA elongation in wax synthesis. The results provide an insight into the molecular mechanisms of fruit brightness in eggplants and further eggplant breeding programs. [ABSTRACT FROM AUTHOR]

Background and Aims: An increasing number of high-risk patients with coronary heart disease (similar to acute myocardial infarction (AMI)) are using PCSK9 inhibitors. However, whether PCSK9 affects myocardial repair and the molecular mechanism of PCSK9 modulation of immune inflammation after AMI are not known. The present research investigated the role of PCSK9 in the immunomodulation of macrophages after AMI and provided evidence for the clinical application of PCSK9 inhibitors after AMI to improve cardiac repair.Methods and Results: Wild-type C57BL6/J (WT) and PCSK9-/- mouse hearts were subjected to left anterior descending (LAD) coronary artery occlusion to establish an AMI model. Correlation analysis showed that higher PCSK9 expression indicated worse cardiac function after AMI, and PCSK9 knockout reduced infarct size, improved cardiac function, and attenuated inflammatory cell infiltration compared to WT mice. Notably, the curative effects of PCSK9 inhibition were abolished after the systemic depletion of macrophages using clodronate liposomes. PCSK9 showed a regulatory effect on macrophage polarization in vivo and in vitro. Our studies also revealed that activation of the TLR4/MyD88/NF-κB axis was a possible mechanism of PCSK9 regulation of macrophage polarization.Conclusion: Our data suggested that PCSK9 modulated macrophage polarization-mediated ventricular remodeling after myocardial infarction. [ABSTRACT FROM AUTHOR]

Objective: Even with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads. Methods: In this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well. Results: At the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus. Conclusion: The results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders. [ABSTRACT FROM AUTHOR]

Browning has been the primary limitation in eggplant processing. This study investigates the molecular mechanism underlying fresh-cut eggplant fruit browning by observing the physicochemical characteristics of browning-resistant ('F') and browning-sensitive ('36′) eggplant cultivars. Browning-related enzyme activity and gene expression (PPO, LOX, and PLD) were significantly higher in the '36′ eggplant, thereby enhancing the degree of browning, compared to the 'F' eggplant. The MDA content and O2− production rate progressively increased as browning increased, while the antioxidant capacity of the fruit decreased. The cutting injury significantly activated the expression of PAL, thereby inducing the accumulation of phenolic acids, while the PPO gene was significantly upregulated, which activated the activity of polyphenol oxidase. Our results showed that the oxidation of chlorogenic acids to chlorogenic quinones resulted in the occurrence of browning, which suggests chlorogenic acid as the main browning substrate in fresh-cut eggplant. [ABSTRACT FROM AUTHOR]

The finite‐time formation tracking control is investigated for a multi‐agent system (MAS) with obstacle avoidance. For the collision and obstacle avoidance problem in the formation process, the artificial potential field is used as the formation planning design, and the virtual structure is adopted to improve the organizational ability of the formation. The trajectory tracking control follows the back‐stepping scheme, and the finite‐time technique is developed in the control design. Considering the dynamics uncertainty of the agent system, a neural network is applied for estimating and the prediction error‐based adaptive law is established to achieve the precise estimation performance. Moreover, the predefined performance function is embedded to satisfy the output constraint. The uniformly ultimate boundedness of the system error signals and the finite‐time convergence of the MAS are guaranteed. The simulation study is performed to validate the proposed control for multiple autonomous underwater vehicles system, while the results manifest that the obstacle avoidance with high‐precision tracking and formation performance will be achieved under the formation trajectory tracking controller. [ABSTRACT FROM AUTHOR]

Motivation Biomedical language models produce meaningful concept representations that are useful for a variety of biomedical natural language processing (bioNLP) applications such as named entity recognition, relationship extraction and question answering. Recent research trends have shown that the contextualized language models (e.g. BioBERT, BioELMo) possess tremendous representational power and are able to achieve impressive accuracy gains. However, these models are still unable to learn high-quality representations for concepts with low context information (i.e. rare words). Infusing the complementary information from knowledge-bases (KBs) is likely to be helpful when the corpus-specific information is insufficient to learn robust representations. Moreover, as the biomedical domain contains numerous KBs, it is imperative to develop approaches that can integrate the KBs in a continual fashion. Results We propose a new representation learning approach that progressively fuses the semantic information from multiple KBs into the pretrained biomedical language models. Since most of the KBs in the biomedical domain are expressed as parent-child hierarchies, we choose to model the hierarchical KBs and propose a new knowledge modeling strategy that encodes their topological properties at a granular level. Moreover, the proposed continual learning technique efficiently updates the concepts representations to accommodate the new knowledge while preserving the memory efficiency of contextualized language models. Altogether, the proposed approach generates knowledge-powered embeddings with high fidelity and learning efficiency. Extensive experiments conducted on bioNLP tasks validate the efficacy of the proposed approach and demonstrates its capability in generating robust concept representations. [ABSTRACT FROM AUTHOR]

Motivation Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. Results We propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways: First, by classifying the cell line, antibody or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. Availability and implementation https://github.com/databio/regionset-embedding. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Fresh-cut processing promotes enzymatic browning of fresh fruits and vegetables, which negatively affects the product appearance and impacts their nutrition. We used RNA-sequencing to analyze the transcriptomic changes occurring during the browning of fresh-cut eggplant fruit samples from both browning-sensitive and browning-resistant cultivars to investigate the molecular mechanisms involved in browning. A total of 8347 differentially expressed genes were identified, of which 62 genes were from six gene families (i.e., PPO, PAL, POD, CAT, APX, and GST) potentially associated with enzymatic browning. Furthermore, using qRT-PCR, we verified 231 differentially regulated transcription factors in fresh-cut eggplant fruits. The enzyme activities of PPO, POD, PAL, and CAT in '36' were significantly higher than those of 'F' fresh-cut for 15 min. Both PPO and POD play a major role in the browning of eggplant pulp and might therefore act synergistically in the browning process. Meanwhile, qPCR results of 18 browning related genes randomly screened in 15 eggplant materials with different browning tolerance showed variant-specific expression of genes. Lastly, gene regulatory networks were constructed to identify the browning-related genes. This work provides a basis for future molecular studies of eggplants, and lays a theoretical foundation for the development of browning-resistant fresh-cut fruits and vegetables. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine if people living with HIV (PLWH) in preclinical human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND), with no clinical symptoms and without decreased daily functioning, suffer from brain volumetric alterations and its patterns. Method: Fifty-nine male PLWH at the HAND preclinical stage were evaluated, including 19 subjects with asymptomatic neurocognitive impairment (ANI), 17 subjects with cognitive abnormality that does not reach ANI (Not reach ANI), and 23 subjects with cognitive integrity. Moreover, 23 healthy volunteers were set as the seronegative normal controls (NCs). These individuals underwent sagittal three-dimensional T1-weighted imaging (3D T1WI). Quantified data and volumetric measures of brain structures were automatically segmented and extracted using AccuBrain®. In addition, the multiple linear regression analysis was performed to analyze the relationship of volumes of brain structures and clinical variables in preclinical HAND, and the correlations of the brain volume parameters with different cognitive function states were assessed by Pearson's correlation analysis. Results: The significant difference was shown in the relative volumes of the ventricular system, bilateral lateral ventricle, thalamus, caudate, and left parietal lobe gray matter between the preclinical HAND and NCs. Furthermore, the relative volumes of the bilateral thalamus in preclinical HAND were negatively correlated with attention/working memory (left: r = −0.271, p = 0.042; right: r = −0.273, p = 0.040). Higher age was associated with increased relative volumes of the bilateral lateral ventricle and ventricular system and reduced relative volumes of the left thalamus and parietal lobe gray matter. The lower CD4+/CD8+ ratio was associated with increased relative volumes of the left lateral ventricle and ventricular system. Longer disease course was associated with increased relative volumes of the bilateral thalamus. No significant difference was found among preclinical HAND subgroups in all indices, and the difference between the individual groups (Not reach ANI and Cognitive integrity groups) and NCs was also insignificant. However, there was a significant difference between ANI and NCs in the relative volumes of the bilateral caudate and lateral ventricle. Conclusion: Male PLWH at the HAND preclinical stage suffer from brain volumetric alterations. AccuBrain® provides potential value in evaluating HIV-related neurocognitive dysfunction. [ABSTRACT FROM AUTHOR]

Motivation Many real-world biomedical interactions such as 'gene-disease', 'disease-symptom' and 'drug-target' are modeled as a bipartite network structure. Learning meaningful representations for such networks is a fundamental problem in the research area of Network Representation Learning (NRL). NRL approaches aim to translate the network structure into low-dimensional vector representations that are useful to a variety of biomedical applications. Despite significant advances, the existing approaches still have certain limitations. First, a majority of these approaches do not model the unique topological properties of bipartite networks. Consequently, their straightforward application to the bipartite graphs yields unsatisfactory results. Second, the existing approaches typically learn representations from static networks. This is limiting for the biomedical bipartite networks that evolve at a rapid pace, and thus necessitate the development of approaches that can update the representations in an online fashion. Results In this research, we propose a novel representation learning approach that accurately preserves the intricate bipartite structure, and efficiently updates the node representations. Specifically, we design a customized autoencoder that captures the proximity relationship between nodes participating in the bipartite bicliques (2 × 2 sub-graph), while preserving both the global and local structures. Moreover, the proposed structure-preserving technique is carefully interleaved with the central tenets of continual machine learning to design an incremental learning strategy that updates the node representations in an online manner. Taken together, the proposed approach produces meaningful representations with high fidelity and computational efficiency. Extensive experiments conducted on several biomedical bipartite networks validate the effectiveness and rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative target enclosing of multiple unicycle-type mobile robots subject to input disturbances. The objective is to make all robots orbit around a given stationary target, and maintain evenly spaced along a common circle. The network of the multirobot systems is set in a cyclic pursuit manner. A dynamic control law is developed for the cooperative target enclosing of the multirobot systems, while tackling the heterogeneous input disturbances generated by linear exogenous systems. The proposed control law requires each robot to use the relative displacement measurements with respect to the target and its neighbors. It is shown that global asymptotic stability of the closed-loop multirobot systems can be guaranteed in the presence of a large class of input disturbance signals. Finally, simulation results illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Underwater visible light communication (UVLC) is a promising technique to provide high-speed data transmission in the water. Since it is quite difficult to supply power to the underwater devices in the complex and severe marine environment, energy consumption would be a main consideration in the practical deployment of UVLC system. In this article, we investigate an asymmetric clipped optical orthogonal frequency division multiplexing and non-orthogonal multiple access (ACO-OFDM-NOMA) scheme for the uplink UVLC system to realize energy saving and support massive device connectivity. A mixed integer nonlinear programming problem of jointly optimizing the user-subcarrier pairing and power allocation is formulated to maximize the system utility. To tackle this problem, we propose a low-complexity iterative algorithm to obtain an optimal solution. Simulation results verify that the proposed algorithm converges quickly, and the uplink ACO-OFDM-NOMA system using the proposed algorithm has better utility compared to the uplink ACO-OFDM-NOMA system using the greedy algorithm and the traditional uplink ACO-OFDM access (ACO-OFDMA) system. [ABSTRACT FROM AUTHOR]

Enzymatic browning is one of the crucial problems compromising the flavor and texture of fresh-cut fruit and vegetables. In this study, an untargeted metabolomics approach based on liquid chromatography-mass spectrometry (LC–MS) was used to explore the browning mechanism in fresh-cut eggplant. Metabolomics studies showed that with the increase of fresh-cut time, the contents of 946 metabolites changed dynamically. The metabolites having the same trend share common metabolic pathways. As an important browning substrate, the content of chlorogenic acid increased significantly, suggesting that may be more important to fresh-cut eggplant browning; all 119 common differential metabolites in 5 min/CK and 3 min/CK contrastive groups were mapped onto 31 KEGG pathways including phenylpropanol metabolism, glutathione metabolism pathway, et al. In physiological experiments, results showed that the Phenylpropanoid-Metabolism-Related enzymes (PAL, C4H, 4CL) were changed after fresh-cut treatment, the activities of three enzymes increased first and then decreased, and reached the maximum value at 5 min, indicating the accumulation of phenolic substances. At the same time, ROS were accumulated when plant tissue damaged by cutting, the activities of related antioxidant enzymes (SOD, APX and CAT) changed dynamically after oxidative damage. SOD and APX content increased significantly and reached the maximum value at 10 min after cutting, and then showed a downward trend. However, CAT activity increased sharply and reached the maximum value within 3 min after cutting, then maintained the same activity, and showed a downward trend after 30 min. These data fully demonstrated that the activities of browning related enzymes and gene expression increased with the prolonging of fresh cutting time. We explained the browning mechanism of fresh-cut eggplant by combining metabolomics and physiology, which may lay the foundation for better understanding the mechanism of browning during the fruits and vegetables during processing. [ABSTRACT FROM AUTHOR]

Turbidity of water significantly affect the system performance of underwater wireless optical communication (UWOC). In this letter, we proposed and experimentally demonstrated a loss-tolerant UWOC system using a compact high-power 520-nm laser diode (LD) and a highly sensitive silicon photomultiplier (SiPM). The LD is directly modulated by the on-off keying-non-return-to-zero (OOK-NRZ) data. A precise collimation system is designed in the transmitter so that the maximum beam divergence angle is fixed at 0.75 mrad. The performance of the proposed system is investigated over a 24-m underwater channel with different attenuation lengths (ALs) mimicking the different levels of water turbidity in coastal water. Experimental results show that the data rates of 20 Mbps, 40 Mbps, 80 Mbps and 100 Mbps can be achieved under 14.3 AL, 13.2 AL, 13.2 AL and 12.3 AL with bit error rates (BERs) lower than the forward error correction (FEC) limit. [ABSTRACT FROM AUTHOR]

The dynamic network data have become ubiquitous with the rapid development of Internet and smart devices. To effectively manage the involved vertices in networks, it is crucial to track the special community patterns and analyze the relationships among vertices. In this paper, we propose a new method to measure the coherence strength, also referred to as community consistency, of a community over a specific observation period. The measurement of community consistency is especially challenging given the dynamic community structure over time, i.e., vertices can leave their original communities and join new communities. In order to interpret the causes of evolving community structure and model the influence of evolving community structure on community consistency, we introduce an influence propagation process having a causal relation with the community consistency. Specifically, a generative model is proposed to combine the influence propagation and the network topological structure at each time step. The proposed influence-based approach for modeling evolution can be instantiated in a variety of real-world network data. The comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency. Besides, we conduct a case study to show the effectiveness of the proposed method in real-world applications. [ABSTRACT FROM AUTHOR]

Global warming induces heat stress in eggplant, seriously affecting its quality and yield. The response to heat stress is a complex regulatory process; however, the exact mechanism in eggplant is unknown. We analyzed the transcriptome of eggplant under different high-temperature treatments using RNA-Seq technology. Three libraries treated at high temperatures were generated and sequenced. There were 40,733,667, 40,833,852, and 40,301,285 clean reads with 83.98%, 79.69%, and 84.42% of sequences mapped to the eggplant reference genome in groups exposed to 28°C (CK), 38°C (T38), and 43°C (T43), respectively. There were 3,067 and 1,456 DEGs in T38 vs CK and T43 vs CK groups, respectively. In these two DEG groups, 315 and 342 genes were up- and down-regulated, respectively, in common. Differential expression patterns of DEGs in antioxidant enzyme systems, detoxication, phytohormones, and transcription factors under heat stress were investigated. We screened heat stress-related genes for further validation by qRT-PCR. Regulation mechanisms may differ under different temperature treatments, in which heat shock proteins and heat stress transcription factors play vital roles. These results provide insight into the molecular mechanisms of the heat stress response in eggplant and may be useful in crop breeding. [ABSTRACT FROM AUTHOR]

Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients. [ABSTRACT FROM AUTHOR]

Enzymes of the chalcone synthase (CHS) family participate in the synthesis of multiple secondary metabolites in plants, fungi and bacteria. CHS showed a significant correlation with the accumulation patterns of anthocyanin. The peel color, which is primarily determined by the content of anthocyanin, is an economically important trait for eggplants that is affected by heat stress. A total of 7 CHS (SmCHS1-7) putative genes were identified in a genome-wide analysis of eggplants (S. melongena L.). The SmCHS genes were distributed on 7 scaffolds and were classified into 3 clusters. Phylogenetic relationship analysis showed that 73 CHS genes from 7 Solanaceae species were classified into 10 groups. SmCHS5, SmCHS6 and SmCHS7 were continuously down-regulated under 38°C and 45°C treatment, while SmCHS4 was up-regulated under 38°C but showed little change at 45°C in peel. Expression profiles of key anthocyanin biosynthesis gene families showed that the PAL, 4CL and AN11 genes were primarily expressed in all five tissues. The CHI, F3H, F3'5'H, DFR, 3GT and bHLH1 genes were expressed in flower and peel. Under heat stress, the expression level of 52 key genes were reduced. In contrast, the expression patterns of eight key genes similar to SmCHS4 were up-regulated at a treatment of 38°C for 3 hour. Comparative analysis of putative CHS protein evolutionary relationships, cis-regulatory elements, and regulatory networks indicated that SmCHS gene family has a conserved gene structure and functional diversification. SmCHS showed two or more expression patterns, these results of this study may facilitate further research to understand the regulatory mechanism governing peel color in eggplants. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law. [ABSTRACT FROM AUTHOR]

Background: Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link different layers of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering intricate relationships among molecular features. However, due to the "big p, small n" problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging. Results: We developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks. Our method learns feature and patient embeddings simultaneously with deep representation learning. Both feature representations and patient representations are subject to certain constraints specified as regularization terms in the training objective. By incorporating domain knowledge into the training objective, we implicitly introduced a good inductive bias into the machine learning model, which helps improve model generalizability. We performed extensive experiments on the TCGA datasets and demonstrated the power of integrating multi-omics data and biological interaction networks using our proposed method for predicting target clinical variables. Conclusions: To alleviate the overfitting problem in deep learning on multi-omics data with the "big p, small n" problem, it is helpful to incorporate biological domain knowledge into the model as inductive biases. It is very promising to design machine learning models that facilitate the seamless integration of large-scale multi-omics data and biomedical domain knowledge for uncovering intricate relationships among molecular features and clinical features. [ABSTRACT FROM AUTHOR]

Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI), and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. ACOEC-FD then achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search for connections between structurally connected regions. Through simulation studies on generated datasets and real fMRI-DTI datasets, we demonstrate that the proposed approach results in improved inference results on EC compared to some methods that only used fMRI data. [ABSTRACT FROM AUTHOR]

Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI), and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. ACOEC-FD then achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search for connections between structurally connected regions. Through simulation studies on generated datasets and real fMRI-DTI datasets, we demonstrate that the proposed approach results in improved inference results on EC compared to some methods that only used fMRI data. [ABSTRACT FROM AUTHOR]

Motivation MEDLINE is the primary bibliographic database maintained by National Library of Medicine (NLM). MEDLINE citations are indexed with Medical Subject Headings (MeSH), which is a controlled vocabulary curated by the NLM experts. This greatly facilitates the applications of biomedical research and knowledge discovery. Currently, MeSH indexing is manually performed by human experts. To reduce the time and monetary cost associated with manual annotation, many automatic MeSH indexing systems have been proposed to assist manual annotation, including DeepMeSH and NLM's official model Medical Text Indexer (MTI). However, the existing models usually rely on the intermediate results of other models and suffer from efficiency issues. We propose an end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms. Each MeSH probe enables the model to extract one specific aspect of biomedical knowledge from an input article, thus comprehensive biomedical information can be extracted with different MeSH probes and interpretability can be achieved at word level. MeSH terms are finally recommended with a unified classifier, making MeSHProbeNet both time efficient and space efficient. Results MeSHProbeNet won the first place in the latest batch of Task A in the 2018 BioASQ challenge. The result on the last test set of the challenge is reported in this paper. Compared with other state-of-the-art models, such as MTI and DeepMeSH, MeSHProbeNet achieves the highest scores in all the F-measures, including Example Based F-Measure, Macro F-Measure, Micro F-Measure, Hierarchical F-Measure and Lowest Common Ancestor F-measure. We also intuitively show how MeSHProbeNet is able to extract comprehensive biomedical knowledge from an input article. [ABSTRACT FROM AUTHOR]

The emergence of functional magnetic resonance imaging (fMRI) provides a good opportunity for brain functional parcellation. However, the high dimension and low signal-to-noise ratio of fMRI data brings difficulties to the existing parcellation methods. To address the issue, this paper presents a novel brain functional parcellation method based on artificial bee colony clustering (ABCC) algorithm with self-adaptive crossover and stepwise search (called CSABCC). In CSABCC, the preprocessed fMRI data is first mapped into a low-dimensional space by spectral mapping to reduce its dimension and each food source position is encoded as a clustering solution composed of cluster centers. Then, CSABCC utilizes an improved artificial bee colony search procedure with some robustness advantage to seek better food sources, where a self-adaptive crossover is employed to enhance information exchange between individuals and onlooker bees adopt a stepwise search to improve its search capability. Finally, a functional parcellation result is obtained by mapping cluster labels onto the corresponding voxels. The experiments on simulated fMRI data show that CSABCC can generate the parcellation closest to the real result, and these results on real insula fMRI data also demonstrate that CSABCC has better search capability and can produce parcellation structures with stronger functional consistency and regional continuity compared to some other typical algorithms. Moreover, the correctness of the parcellation results is also validated by functional connectivity fingerprints of the corresponding subregions. [ABSTRACT FROM AUTHOR]

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates. [ABSTRACT FROM AUTHOR]

The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual inspection from long-term EEG readings, automatic EEG seizure detection has garnered increasing attention among researchers. In this paper, we present a unified multi-view deep learning framework to capture brain abnormalities associated with seizures based on multi-channel scalp EEG signals. The proposed approach is an end-to-end model that is able to jointly learn multi-view features from both unsupervised multi-channel EEG reconstruction and supervised seizure detection via spectrogram representation. We construct a new autoencoder-based multi-view learning model by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information. By adding a channel-wise competition mechanism in the training phase, we propose a channel-aware seizure detection module to guide our multi-view structure to focus on important and relevant EEG channels. To validate the effectiveness of the proposed framework, extensive experiments against nine baselines, including both traditional handcrafted feature extraction and conventional deep learning methods, are carried out on a benchmark scalp EEG dataset. Experimental results show that the proposed model is able to achieve higher average accuracy and f1-score at 94.37% and 85.34%, respectively, using 5-fold subject-independent cross validation, demonstrating a powerful and effective method in the task of EEG seizure detection. [ABSTRACT FROM AUTHOR]

Predicting patients’ risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The electric health records (EHRs), which are irregularly sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to the lack of an appropriate representation. Moreover, there needs an effective approach to measure patient similarity on EHRs. In this paper, we propose two novel deep similarity learning frameworks which simultaneously learn patient representations and measure pairwise similarity. We use a convolutional neural network (CNN) to capture local important information in EHRs and then feed the learned representation into triplet loss or softmax cross entropy loss. After training, we can obtain pairwise distances and similarity scores. Utilizing the similarity information, we then perform disease predictions and patient clustering. Experimental results show that CNN can better represent the longitudinal EHR sequences, and our proposed frameworks outperform state-of-the-art distance metric learning methods. [ABSTRACT FROM AUTHOR]

Motivation: The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. Results: We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top-K results. This level of efficiency enables the discovery algorithm to look for higher-order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to perform both open and closed discovery. We also experimentally validate that the core data-structures upon which the system bases its decision has a high concordance with the opinion of the experts. This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. [ABSTRACT FROM AUTHOR]

Identifying functional modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting functional modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for functional module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein modules and help biologists to find some novel biological insights. [ABSTRACT FROM AUTHOR]