AIMC Topic: Machine Learning

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MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains li...

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-...

TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep...

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Briefings in bioinformatics
Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph conv...

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.

Briefings in bioinformatics
Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey...

Contrastive learning for enhancing feature extraction in anticancer peptides.

Briefings in bioinformatics
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering reco...

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Briefings in bioinformatics
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of ...