AIMC Topic: Computational Biology

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iDRBP-ECHF: Identifying DNA- and RNA-binding proteins based on extensible cubic hybrid framework.

Computers in biology and medicine
Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to accurately and efficiently identify nucleic acid-binding proteins (NABPs) is particularly significant. Some sequence-based computational methods have...

Glycoinformatics in the Artificial Intelligence Era.

Chemical reviews
Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and t...

Construction of a Deep Neural Network Energy Function for Protein Physics.

Journal of chemical theory and computation
The traditional approach of computational biology consists of calculating molecule properties by using approximate classical potentials. Interactions between atoms are described by an energy function derived from physical principles or fitted to expe...

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction e...

IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some prote...

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, f...

Improved prediction of gene expression through integrating cell signalling models with machine learning.

BMC bioinformatics
BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically ...

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Nature protocols
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-d...

A deep learning framework for identifying essential proteins based on multiple biological information.

BMC bioinformatics
BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interacti...