AIMC Topic: Computational Biology

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Chemical-induced disease relation extraction with dependency information and prior knowledge.

Journal of biomedical informatics
Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have ...

Assessment of Functional Phosphatidylinositol 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based Computational Model.

The American journal of pathology
The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but reliable diagnostic tests that assess functional PI3K activity are lacking. Because PI3K pathway activi...

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

BMC genomics
BACKGROUND: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence ...

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.

Journal of proteome research
Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to...

Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.

Scientific reports
Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However ther...

Towards better prediction of Mycobacterium tuberculosis lineages from MIRU-VNTR data.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
The determination of lineages from strain-based molecular genotyping information is an important problem in tuberculosis. Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing is a commonly used molecular genotyp...

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...

A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...

Drug Selection via Joint Push and Learning to Rank.

IEEE/ACM transactions on computational biology and bioinformatics
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this article, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to ac...

Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.

IEEE/ACM transactions on computational biology and bioinformatics
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, diff...