AIMC Topic: Computational Biology

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Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Scientific reports
Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical ...

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning.

Genomics, proteomics & bioinformatics
Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination ...

Machine learning models for drug-target interactions: current knowledge and future directions.

Drug discovery today
Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compound...

DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet.

Journal of chemical information and modeling
Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, the use of deep neural n...

ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.

Journal of molecular biology
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system...

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of derivi...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

Scientific reports
DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG,...

Review and comparative analysis of machine learning-based phage virion protein identification methods.

Biochimica et biophysica acta. Proteins and proteomics
Phage virion protein (PVP) identification plays key role in elucidating relationships between phages and hosts. Moreover, PVP identification can facilitate the design of related biochemical entities. Recently, several machine learning approaches have...

The hunt for sORFs: A multidisciplinary strategy.

Experimental cell research
Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their func...

DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides.

IEEE journal of biomedical and health informatics
Antiviral peptides (AVPs) have been experimentally verified to block virus into host cells, which have antiviral activity with decapeptide amide. Therefore, utilization of experimentally validated antiviral peptides is a potential alternative strateg...