AIMC Topic: Computational Biology

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Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

Computational biology and chemistry
BACKGROUND: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target inte...

BetaDL: A protein beta-sheet predictor utilizing a deep learning model and independent set solution.

Computers in biology and medicine
The sequence-based prediction of beta-residue contacts and beta-sheet structures contain key information for protein structure prediction. However, the determination of beta-sheet structures poses numerous challenges due to long-range beta-residue in...

Discrimination of contagious and environmental strains of Streptococcus uberis in dairy herds by means of mass spectrometry and machine-learning.

Scientific reports
Streptococcus uberis is one of the most common pathogens of clinical mastitis in the dairy industry. Knowledge of pathogen transmission route is essential for the selection of the most suitable intervention. Here we show that spectral profiles acquir...

Some Similarity Measures of Neutrosophic Sets Based on the Euclidean Distance and Their Application in Medical Diagnosis.

Computational and mathematical methods in medicine
Similarity measure is an important tool in multiple criteria decision-making problems, which can be used to measure the difference between the alternatives. In this paper, some new similarity measures of single-valued neutrosophic sets (SVNSs) and in...

A multi-context learning approach for EEG epileptic seizure detection.

BMC systems biology
BACKGROUND: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroen...

Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors.

IEEE/ACM transactions on computational biology and bioinformatics
Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleoti...

Computational prediction of inter-species relationships through omics data analysis and machine learning.

BMC bioinformatics
BACKGROUND: Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the us...

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

BMC medical genomics
BACKGROUND: With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensat...

Computational discovery of direct associations between GO terms and protein domains.

BMC bioinformatics
BACKGROUND: Families of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, a...

Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks.

PLoS computational biology
Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barco...