Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative reduce the total time and cost of traditional drug development. Many computational strategies repositioning have been proposed, are based on similarities among drugs diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or disease-related phenotypes) calculate disease similarity, respectively, while not taking into account influence...

Brain tumor segmentation aims to separate the different tissues such as active cells, necrotic core, and edema from normal brain of White Matter (WM), Gray (GM), Cerebrospinal Fluid (CSF). MRI-based studies are attracting more attention in recent years due non-invasive imaging good soft tissue contrast Magnetic Resonance Imaging (MRI) images. With development almost two decades, innovative approaches applying computer-aided techniques for segmenting becoming mature coming closer routine...

Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well treatment. Although many computational methods have been developed, precisely identifying associations, especially for novel lncRNAs, remains challenging. In this study, we propose a method (named SIMCLDA)...

Abstract Motivation Protein–protein interactions (PPIs) play important roles in many biological processes. Conventional experiments for identifying PPI sites are costly and time-consuming. Thus, computational approaches have been proposed to predict sites. Existing methods usually use local contextual features Actually, global of protein sequences critical site prediction. Results A new end-to-end deep learning framework, named DeepPPISP, through combining sequence features, is For we a...

CircR2Disease is a manually curated database, which provides comprehensive resource for circRNA deregulation in various diseases. Increasing evidences have shown that circRNAs play critical roles transcriptional, post-transcriptional and translational regulation. Therefore, the aberrant expression of has been associated with group It significant to develop high-quality database deposit deregulated The current version contains 725 associations between 661 100 diseases by reviewing existing...

Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods been developed infer associations. However, most these only based on single data resource.In this paper, we propose a new method predict by integrating multiple biological resources. Then, implement as web server for...

Regions of interest (ROIs) based classification has been widely investigated for analysis brain magnetic resonance imaging (MRI) images to assist the diagnosis Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) MCI converted AD (MCIc) not (MCInc). Since an ROI representation structures is obtained either by pre-definition or adaptive parcellation, corresponding in different brains can be measured. However, due noise small sample...

Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) visual transformer have been adopted as two branches learn features for by simply adding local global features. However, such simple addition may not be effective integrate these In this study, we explore synergy images tasks. Specifically, design a Deep Integrated Feature Fusion (DIFF) block synergize from CNN branch branch. Our...

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with inequality and linear equality constraints. Since objective function constraints may not be smooth, Clarke's generalized gradients of are employed to describe dynamics network. It proved that equilibrium point set equivalent optimal solution original by using Lagrangian saddle-point theorem. Under weak conditions, stable, state convergent one its points. Compared existing models problems, can...

Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed predicting by using topological features protein-protein interaction (PPI) networks. However, most these ignored intrinsic biological meaning proteins. Moreover, PPI data contains many false positives negatives. To overcome limitations, recently research groups started to focus on identification...

Abstract Motivation The development of single-cell RNA-sequencing (scRNA-seq) provides a new perspective to study biological problems at the level. One key issues in scRNA-seq analysis is resolve heterogeneity and diversity cells, which cluster cells into several groups. However, many existing clustering methods are designed analyze bulk RNA-seq data, it urgent develop methods. Moreover, high noise data also brings lot challenges computational Results In this study, we propose novel cell...

Cluster analysis of biological networks is one the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization clustering results crucial to uncover structure networks. In this paper, ClusterViz, an APP Cytoscape 3 cluster visualization, has been developed. order reduce complexity enable extendibility we designed architecture ClusterViz based on framework Open Services Gateway Initiative. According architecture, implementation...

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, regions their interactions can be modeled network, which describe highly efficient information transmission in a brain. Therefore, network analysis plays an important role the study of diseases. With development noninvasive neuroimaging electrophysiological techniques, experimental data produced for constructing networks. recent years, researchers have found...

MicroRNAs (miRNAs) are a type of non-coding RNAs with about ~22nt nucleotides. Increasing evidences have shown that miRNAs play critical roles in many human diseases. The identification disease-related is helpful to explore the underlying pathogenesis More and more experimental validated associations between diseases been reported recent studies, which provide useful information for new miRNA-disease association discovery. In this study, we propose computational framework, KBMF-MDI, predict...

ICD-9 (the Ninth Revision of International Classification Diseases) is widely used to describe a patient's diagnosis. Accurate automated coding important because manual expensive, time-consuming, and inefficient. Inspired by the recent successes deep learning, in this study, we present learning framework called DeepLabeler automatically assign codes. combines convolutional neural network with `Document Vector' technique extract encode local global features. Our proposed demonstrates its...

Circular RNAs (circRNAs) are a large group of endogenous non-coding which key members gene regulatory processes.Those circRNAs in human paly significant roles health and diseases.Owing to the characteristics their universality, specificity stability, becoming an ideal class biomarkers for disease diagnosis, treatment prognosis.Identification relationships between diseases can help understand complex mechanism.However, traditional experiments costly time-consuming, little computational models...

High-throughput screening technologies have provided a large amount of drug sensitivity data for panel cancer cell lines and hundreds compounds. Computational approaches to analyzing these can benefit anticancer therapeutics by identifying molecular genomic determinants developing new drugs. In this study, we developed deep learning architecture improve the performance prediction based on data. We integrated both features chemical information compounds predict half maximal inhibitory...

Nowadays, cluster analysis of biological networks has become one the most important approaches to identifying functional modules as well predicting protein complexes and network biomarkers. Furthermore, visualization clustering results is crucial display structure networks. Here we present CytoCluster, a cytoscape plugin integrating six algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping Hierarchical IPCA (Identifying...