AIMC Topic: Computational Biology

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Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.

Nucleic acids research
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biologica...

Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.

Frontiers in bioscience (Landmark edition)
: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments ...

Topological network measures for drug repositioning.

Briefings in bioinformatics
Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a ke...

A comprehensive survey on computational methods of non-coding RNA and disease association prediction.

Briefings in bioinformatics
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor ...

Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.

Briefings in bioinformatics
A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core prom...

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Briefings in bioinformatics
Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechan...

Predicting enhancer-promoter interactions by deep learning and matching heuristic.

Briefings in bioinformatics
Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In th...

Prediction of driver variants in the cancer genome via machine learning methodologies.

Briefings in bioinformatics
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, an...

Predicting drug-disease associations through layer attention graph convolutional network.

Briefings in bioinformatics
BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient ...