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Complex diseases are caused by a variety of factors, and their diagnosis, treatment and prognosis are usually difficult. Proteins play an indispensable role in living organisms and perform specific biological functions by interacting with other proteins or biomolecules, their dysfunction may lead to diseases, it is a natural way to mine disease-related biomarkers from protein-protein interaction network. AUC, the area under the receiver operating characteristics (ROC) curve, is regarded as a gold standard to evaluate the effectiveness of a binary classifier, which measures the classification ability of an algorithm under arbitrary distribution or any misclassification cost. In this study, we have proposed a network-based multi-biomarker identification method by AUC optimization (NetAUC), which integrates gene expression and the network information to identify biomarkers for the complex disease analysis. The main purpose is to optimize two objectives simultaneously: maximizing AUC and minimizing the number of selected features. We have applied NetAUC to two types of disease analysis: 1) prognosis of breast cancer, 2) classification of similar diseases. The results show that NetAUC can identify a small panel of disease-related biomarkers which have the powerful classification ability and the functional interpretability.

The computer-based approach is required for the non-invasive detection of chronic liver diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we review the computer-aided diagnosis of hepatic lesions in view of diffuse- and focal liver disorders. This survey mainly focuses on three image acquisition modalities: ultrasonography, computed tomography, and magnetic resonance imaging. We present the insightful analysis with pros and cons for each preliminary step, particularly preprocessing, attribute analysis, and classification techniques to accomplish clinical diagnostic tasks. In preprocessing, we explore and compare commonly used denoising, deblurring and segmentation methods. Denoising is mainly performed with nonlinear models. In contrast, deep neural networks are frequently applied for deblurring and automatic segmentation of region-of-interest. In attribute analysis, the most common approach comprises texture properties. For classification, the support vector machine is mainly utilized across three image acquisition modalities. However, comparative analysis shows the best performance is obtained by deep learning-based convolutional neural networks. Considering biopsy samples or pathological factors such as overall stage, margin, and differentiation can be helpful for improving the prediction performance. In addition, technique breakthrough is expected soon with advances in machine learning models to address data limitation problems and improve the prediction performance.

In modern neuroscience and clinical study, neuroscientists and clinicians often use non-invasive imaging techniques to validate theories and computational models, observe brain activities and diagnose brain disorders. The functional Magnetic Resonance Imaging (fMRI) is one of the commonly-used imaging modalities that can be used to understand human brain mechanisms as well as the diagnosis and treatment of brain disorders. The advances in artificial intelligence and the emergence of deep learning techniques have shown promising results to better interpret fMRI data. Deep learning techniques have rapidly become the state of the art for analyzing fMRI data sets and resulted in performance improvements in diverse fMRI applications. Deep learning is normally presented as an end-to-end learning process and can alleviate feature engineering requirements and hence reduce domain knowledge requirements to some extent. Under the framework of deep learning, fMRI data can be considered as images, time series or images series. Hence, different deep learning models such as convolutional neural networks, recurrent neural network, or a combination of both, can be developed to process fMRI data for different tasks. In this review, we discussed the basics of deep learning methods and focused on its successful implementations for brain disorder diagnosis based on fMRI images. The goal is to provide a high-level overview of brain disorder diagnosis with fMRI images from the perspective of deep learning applications.

Automatic International Classification of Diseases (ICD) coding is an important task in the future of artificial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic ICD coding only focus on the semantic features of clinical notes and ignore the feature extraction of ICD titles that are the descriptions of ICD codes. In this paper, we propose a knowledge attention-based deep learning framework called KAICD for automatic ICD coding. KAICD makes full use of the clinic notes and the ICD titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For ICD titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some ICD titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAICD is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III); it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of ICD titles learned by the attention-based Bi-GRU enhances the feature expression and improves the prediction performance.

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In electron microscope segmentation of ISBI challenge 2012, the concise architecture and outstanding performance of the U-shaped network are impressive. Then, a variety of segmentation models based on this architecture have been proposed for medical image segmentations. We present a comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies. The aim of this survey is twofold. First, we report the different extended U-shaped networks, discuss main state-of-the-art extended mechanisms, including residual mechanism, dense mechanism, dilated mechanism, attention mechanism, multi-module mechanism, and ensemble mechanism, analyze their pros and cons. Second, this survey provides the overview of studies in main application areas of U-shaped networks, including brain tumor, stroke, white matter hyperintensities (WMHs), eye, cardiac, liver, musculoskeletal, skin cancer, and neuronal pathology. Finally, we summarize the current U-shaped networks, point out the open challenges and directions for future research.

The classification of mild cognitive impairment (MCI), which is a early stage of Alzheimer’s disease and is associated with brain structural and functional changes, is still a challenging task. Recent studies have shown great promise for improving the performance of MCI classification by combining multiple structural and functional features, such as grey matter volume and clustering coefficient. However, extracting which features and how to combine multiple features to improve the performance of MCI classification have always been challenging problems. To address these problems, in this study we propose a new method to enhance the feature representation of multi-modal MRI data by combining multi-view information to improve the performance of MCI classification. Firstly, we extract two structural features (including grey matter volume and cortical thickness) and two functional features (including clustering coefficient and shortest path length) of each cortical brain region based on automated anatomical labeling (AAL) atlas from both T1w MRI and rs-fMRI data of each subject. Then, in order to obtain features that are more helpful in distinguishing MCI subjects, an improved multi-task feature selection method, namely MTFS-gLASSO-TTR, is proposed. Finally, a multi-kernel learning algorithm is adopted to combine multiple features to perform the MCI classification task. Our proposed MCI classification method is evaluated on 315 subjects (including 105 LMCI subjects, 105 EMCI subjects and 105 NCs) with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed method achieves an accuracy of 88.5% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.897 for LMCI/NC classification, an accuracy of 82.7% and an AUC of 0.832 for EMCI/NC classification, and an accuracy of 79.6% and an AUC of 0.803 for LMCI/EMCI classification, respectively. In addition, by comparison, the accuracy and AUC values of our proposed method are better than those of some existing state-of-the-art methods in MCI classification. Overall, our proposed MCI classification method is effective and promising for automatic diagnosis of MCI in clinical practice.

In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biological processes but also are the foundation for understanding mechanisms of human diseases. Due to the high cost of traditional biological experiments, the number of experimentally verified lncRNA-disease associations is very limited. Thus, many computational approaches have been proposed to discover the underlying associations between lncRNAs and diseases. However, the associations between lncRNAs and diseases are too complicated to model by using only traditional matrix factorization-based methods. In this study, we propose a hybrid computational framework (SDLDA) for the lncRNA-disease association prediction. In our computational framework, we use singular value decomposition and deep learning to extract linear and non-linear features of lncRNAs and diseases, respectively. Then we train SDLDA by combing the linear and non-linear features. Compared to previous computational methods, the combination of linear and non-linear features reinforces each other, which is better than using only either matrix factorization or deep learning. The computational results show that SDLDA has a better performance over existing methods in the leave-one-out cross-validation. Furthermore, the case studies show that 28 out of 30 cancer-related lncRNAs (10 for gastric cancer, 10 for colon cancer and 8 for renal cancer) are verified by mining recent biomedical literature. Code and data can be accessed at https://github.com/CSUBioGroup/SDLDA.

White matter hyperintensities (WMHs) appear as regions of abnormally signal intensity on magnetic resonance imaging (MRI) images, that can be identified in MRI images of elderly people and ischemic stroke patients. However, manual segmentation and quantification of images with WMHs is laborious and time-consuming. Moreover, ischemic stroke lesion and WMHs appear as similar signals in MRI images, making it difficult to accurately segment the WMHs. Analysis of WMH-containing images is important for clinical diagnosis, and thus several segmentation methods have been proposed. However, these methods cannot accurately differentiate WMH and ischemic stroke lesions. We propose a deep convolutional neural network, M2DCNN, that can accurately segment WMHs and distinguish them from ischemic stroke lesions. M2DCNN consists of two subnets that rely on a set of novel multi-scale features and a novel architecture (inclusion of dense and dilated blocks). Our model is trained and evaluated on two public segmentation challenges with multi-modality MRI images. Empirical tests demonstrate that M2DCNN outperforms current segmentation methods. We empirically demonstrate that M2DCNN effectively separates WMHs from stroke lesions. Finally, ablation experiments reveal that both multi-scale features and architectural elements in our method contribute to the improved predictive performance.

Many computational methods have been proposed to predict essential proteins from protein-protein interaction (PPI) networks. However, it is still challenging to improve the prediction accuracy. In this study, we propose a new method, esPOS (essential proteins Predictor using Order Statistics) to predict essential proteins from PPI networks. Firstly, we refine the networks by using gene expression information and subcellular localization information. Secondly, we design some new features, which combine the protein predicted secondary structure with PPI network. We show that these new features are useful to predict essential proteins. Thirdly, we optimize these features by using a greedy method, and combine the optimized features by order statistic method. Our method achieves the prediction accuracy of 0.76-0.79 on two network datasets. The proposed method is available at https://sourceforge.net/projects/espos/.

As manual delineation of lesions in medical image is a very tedious and time consuming process, accurate and automatic segmentation of medical images can assist diagnosis and treatment. In this study, we propose a deep convolution neural network for stroke magnetic resonance imaging(MRI) segmentation. The main structure of our network consists of two symmetrical deep sub-networks, in which dense blocks are embedded for extracting effective features from sparse pixels to alleviate the over-fitting problem of deep networks. We use the multi-kernel to divide the network into two sub-networks for acquiring more receptive fields, and the dropout regularization method to achieve an effective feature mapping. For the post-processing of the soft segmentation, we use image median filtering to alleviate noises and preserve the edge details of images. Our network is evaluated on two public benchmark segmentation challenges (SISS: sub-acute ischemic stroke lesion segmentation and SPES: acute stroke outcome/penumbra estimation) with multi-modality MRI sequences. According to the results of the public benchmark reports, among 9 teams participating in both SISS and SPES challenges at the same time, our network achieves the top performance on SISS challenge, and the top 3 performance on the SPES challenge. In addition, our network also exhibits state-of-the-art performance compared with other segmentation methods. Finally, we extensively evaluate our network with an ablation experiment. The experimental results show that both multi-kernel and dropout strategies can improve the segmentation accuracy of our proposed network.

Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinical free texts. In recent years, with the development of deep learning technology, deep neural networks (DNNs) have been widely used in Chinese clinical named entity recognition and many other clinical NLP tasks. However, these state-of-the-art models failed to make full use of the global information and multi-level semantic features in clinical texts. We design an improved character-level representation approach which integrates the character embedding and the character-label embedding to enhance the specificity and diversity of feature representations. Then, a multi-head self-attention based Bi-directional Long Short-Term Memory Conditional Random Field (MUSA-BiLSTM-CRF) model is proposed. By introducing the multi-head self-attention and combining a medical dictionary, the model can more effectively capture the weight relationships between characters and multi-level semantic feature information, which is expected to greatly improve the performance of Chinese clinical named entity recognition. We evaluate our model on two CCKS challenge (CCKS2017 Task 2 and CCKS2018 Task 1) benchmark datasets and the experimental results show that our proposed model achieves the best performance competing with the state-of-the-art DNN based methods.

Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on scanned abdominal computed tomography (CT) images. In this study, we present an automatic unified registration-free deep-learning-based model with residual block and dilated convolution for training end-to-end liver and lesion segmentation. A multi-scale approach has also been utilized to explore novel inter-slice features with multi-channel input images. A novel objective function is introduced to deal with fore- and background pixels imbalance based on the joint metric of dice coefficient and absolute volumetric difference. Further, batch normalization is used to improve the learning without any loss of useful information. The proposed methodology is extensively validated and tested on 30% of the publicly available Dircadb, LiTS, Sliver07, and Chaos datasets. A comparative analysis is conducted based on multiple evaluation metrics frequently used in segmentation competitions. The results show substantial improvement, with mean dice scores of 97.31, 97.38, 97.39 and 95.49% for the Dircadb, LiTS, Sliver07, and Chaos liver test sets, and 91.92 and 86.70% for Dircadb and LiTS lesion segmentation. It should be noted that we achieve the best lesion segmentation performance on common datasets. The obtained qualitative and quantitative results demonstrate that our proposed model outperform other state-of-the-art methods for liver and lesion segmentation, with competitive performance on additional datasets. Henceforth, it is envisaged as being applicable to pertinent medical segmentation applications.

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC.

Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the prediction model (predictor) for stock daily movement trend prediction. We first build an initial set of potential candidate predictors based on the convolutional long short-term memory networks (ConvLSTMs) by using different values of parameters. To evaluate the candidate predictors, we propose a kernel time-weighted fuzzy c-means clustering algorithm (KTFCM), which improves the kernel FCM algorithm (KFCM), to organize the historical samples according to their relevance to the target sample, which makes the historical samples that are closely related to the target sample have more influence on the predictors. Then, we use the well-organized historical samples to evaluate the candidate predictors. The predictor that yields the best accuracy is selected to predict the target sample. The proposed DPSA algorithm takes less than one minute in total for training the networks, evaluating and selecting the predictors, and performing prediction, which greatly shortens the time of the deep learning prediction. We perform the comparative experiments for the proposed DPSA algorithm and seven popular methods. These experiments test a large real-life financial time series data of various stock markets. The experiment results show that DPSA achieves the best accuracy and the highest return compared to the seven other popular methods.

Protein complexes are very important for investigating the characteristics of biological processes. Identifying protein complexes from proteinprotein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying protein complexes from PPI networks is to detect seed nodes (proteins) from which protein complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying protein complexes, which employs the concept of topology potential to detect significant proteins and mine protein complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin).

Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis. Although there exist several algorithms for SNMF in literature, their convergence and initialization have not been well addressed. In this paper, we first discuss the convergence and initialization of existing algorithms for SNMF. We then propose a Converged Algorithm for SNMF (called CASNMF) which minimizes the Euclidean distance between a symmetrical matrix and its approximation of SNMF. Based on the optimization principle and the local auxiliary function method, we prove that our presented CASNMF does not only converge to a stationary point, but also could be applied to the wider range of SNMF problems. In addition, CASNMF does not require that the initial values are nonzero. To verify our theoretical results, experiments on three data sets are conducted by comparing our proposed CASNMF with other existing methods.

Motivation Cheap and fast next generation sequencing (NGS) technologies facilitate research of de novo assembly greatly. The reliability of contigs is critical to construct reliable scaffolding. However, contigs generated from most assemblers contain errors because of the limitation of assembly strategy and computation complexity. Among all these errors, the misassembly error is one of the most harmful types. Results In this paper, we propose a new method named “PECC” to identify and correct misassembly errors in contigs based on the paired-end read distribution. PECC extracts sequence regions with lower paired-end reads supports and verifies them based on the distribution of paired-end supports. To validate the effectiveness of PECC, we applied PECC to the contigs produced by five popular assemblers on four real datasets, and we also carried out experiments to analyze the influences of PECC on scaffolding. The results show that PECC can reduce misassembly errors and improve the performance of scaffolding results, which demonstrate the promising applications of PECC in de novo assembly.

With the progress of studies and researches on the biological networks, plenty of excellent clustering algorithms have been proposed. Nevertheless, not only different algorithms but also the same algorithms with different characteristics result in different performances on the same biological networks. Therefore, it might be difficult for researchers to choose an appropriate clustering algorithm to use for a specific network. Here we present C-DEVA, a comprehensive platform for Detecting clusters from biological networks and its Evaluation, Visualization and Annotation analysis. Ten clustering methods are provided in C-DEVA, covering different types of clustering algorithms, with a discrepancy in principle of each type. For the identified complexes, there are over ten popular and traditional bio-statistical measurements to assess them. And multi-source biological information has been integrated in C-DEVA, such as biology-functional annotations, and gold standard complex sets, which are collected from latest datasets in major databases or related papers. Furthermore, visualization analyses are available throughout the whole workflow, which endows C-DEVA with good usability and simple manipulation. To assure extensibility, development interfaces are offered in C-DEVA, for integrating new clustering as well as evaluating methods. Additionally, operations to the network as for example network randomization are also supported. C-DEVA provides a complete tool for identifying clusters from biological networks. Multiple options are offered during the analysis process, including detection methods, evaluation metrics and visualization modules. In addition, researchers could customize C-DEVA for the workflow according to the properties of their networks, and find the most ideal results. C-DEVA is released under the GNU General Public License (GPL), and the source code and binaries are freely available at https://github.com/cici333/c-deva .

Identifying protein complexes from Protein-protein Interaction Networks (PINs) is fundamental for understanding protein functions and activities in cell. Based on the assumption that protein complexes are highly connected areas in PINs, many algorithms were proposed to identify protein complexes from PINs. However, most of these approaches neglected that not all proteins in complexes are highly connected, and proteins in PINs with different topological properties may form protein complexes in different ways and should be treated differently. In this paper, we proposed a double-layer clustering method based on the power-law distribution (PLCluster). To calculate the centrality scores of nodes, we proposed a Dense-Spread Centrality method. The centrality scores calculated by Dense-Spread Centrality method follow a power-law distribution. Based on the power-law distribution of the centrality scores, PLCluster divides the nodes into two categories: the nodes with very high centrality scores and the nodes with lower centrality scores. Then different strategies are applied to nodes in different categories for detecting protein complexes from the PIN, respectively. Furthermore, the predicted protein complexes, which are inconsistent with the fact that all proteins in a protein complex should be in the same subcellular compartment, are filtered out. Compared with other nine existing methods on a high reliable yeast PIN, PLCluster shows great advantages in terms of the number of known complexes that are identified, Sensitivity, Specificity, f-measure and the number of perfect matches.

Markov clustering (MCL) is a commonly used algorithm for clustering networks in bioinformatics. It shows good performance in clustering dynamic protein-protein interaction networks (DPINs). However, a limitation of MCL and its variants (e.g, regularized MCL and soft regularized MCL) is that the clustering results are mostly dependent on the parameters whose values are user-specified. In this study, we propose a new MCL method based on the firefly algorithm (FA) to identify protein complexes from DPIN. Based on three-sigma principle, we construct the DPIN and discuss an overall modeling process. In order to optimize parameters, we exploit a number of population-based optimization methods. A thorough comparison completed for different swarm optimization algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) has been carried out. The identified protein complexes on the DIP dataset show that the new algorithm outperforms the state-of-the-art approaches in terms of accuracy of protein complex identification.

Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. New method To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. Results Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. Comparison with existing methods Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. Conclusion The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.

Circular RNAs (circRNAs), a large group of small endogenous noncoding RNA molecules, have been proved to modulate protein-coding genes in the human genome. In recent years, many experimental studies have demonstrated that circRNAs are dysregulated in a number of diseases, and they can serve as biomarkers for disease diagnosis and prognosis. However, it is expensive and time-consuming to identify circRNA-disease associations by biological experiments and few computational models have been proposed for novel circRNA-disease association prediction. In this study, we develop a computational model based on the random walk and the logistic regression (RWLR) to predict circRNA-disease associations. Firstly, a circRNA-circRNA similarity network is constructed by calculating their functional similarity of circRNA based on circRNA-related gene ontology. Then, a random walk with restart is implemented on the circRNA similarity network, and the features of each pair of circRNA-disease are extracted based on the results of the random walk and the circRNA-disease association matrix. Finally, a logistic regression model is used to predict novel circRNA-disease associations. Leave one out validation (LOOCV), five-fold cross validation (5CV) and ten-fold cross validation (10CV) are adopted to evaluate the prediction performance of RWLR, by comparing with the latest two methods PWCDA and DWNN-RLS. The experiment results show that our RWLR has higher AUC values of LOOCV, 5CV and 10CV than the other two latest methods, which demonstrates that RWLR has a better performance than other computational methods. What's more, case studies also illustrate the reliability and effectiveness of RWLR for circRNA-disease association prediction.

It is well known that discovering a new drug is a cumbersome, time-consuming and expensive process. Computational approaches for identifying interactions between drug compounds and target proteins have become important in drug discovery which is helpful to reduce these obstacles. The difficulties of drug-target interaction identification include the lack of known drug-target associations and no experimentally verified negative examples. In this study, we present a method, called PUDT, to predict drug-target interactions. Instead of treating unknown interactions as negative examples, we consider unknown interactions as unlabeled examples. The unlabeled examples are divided into two parts: reliable negative examples and likely negative examples based on protein structure similarity. Then, a weighted support vector machine is used to build a classifier to predict drug-target interactions based on protein sequence and drug structure information. Four data sets (enzymes, ion channels, GPCRs and nuclear receptors) are used to evaluate the performance of the proposed method PUDT. The experimental results demonstrate that our method PUDT outperforms recent state-of-the-art approaches.

Background and scope Nowadays, centrality analysis has become a principal method for identifying essential proteins in biological networks. Here we present CytoNCA, a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measures. Implementation and performance (i) CytoNCA supports eight different centrality measures and each can be applied to both weighted and unweighted biological networks. (ii) It allows users to upload biological information of both nodes and edges in the network, to integrate biological data with topological data to detect specific nodes. (iii) CytoNCA offers multiple potent visualization analysis modules, which generate various forms of output such as graph, table, and chart, and analyze associations among all measures. (iv) It can be utilized to quantitatively assess the calculation results, and evaluate the accuracy by statistical measures. (v) Besides current eight centrality measures, the biological characters from other sources could also be analyzed and assessed by CytoNCA. This makes CytoNCA an excellent tool for calculating centrality, evaluating and visualizing biological networks. Availability http://apps.cytoscape.org/apps/cytonca .

International Classification of Diseases (ICD) code is an important label of electronic health record. The automatic ICD code assignment based on the narrative of clinical documents is an essential task which has drawn much attention recently. When Chinese clinical notes are the input corpus, the nature of Chinese brings some issues that need to be considered, such as the accuracy of word segmentation and the representation of single Chinese characters which contain semantics. Taking the lengthy text of patient notes and the representation of Chinese words into account, we present a multilayer attention bidirectional recurrent neural network (MA-BiRNN) model to implement the assignment of disease codes. A hierarchical approach is used to represent the feature of discharge summaries without manual feature engineering. The combination of character level embedding and word level embedding can improve the representation of words. Attention mechanism is introduced into bidirectional long short term memory networks, which helps to solve the performance dropping problem when plain recurrent neural networks encounter long text sequences. The experiment is carried out on a real-world dataset containing 7732 admission records in Chinese and 1177 unique ICD-10 labels. The proposed model achieves 0.639 and 0.766 in F1-score on full-level code and block-level code, respectively. It outperforms the baseline neural network models and achieves the lowest Hamming loss value. Ablation analysis indicates that the multilevel attention mechanism plays a decisive role in the system for dealing with Chinese clinical notes.

This paper considers identification problems of multirate multiple-input output error systems, derives the input-output representations by using the state space models of the multirate systems, and presents two auxiliary model based recursive least squares algorithms for the corresponding output error models with each subsystem having different or same denominator polynomials. The simulation results show the effectiveness of the proposed algorithms.