AIMC Topic: Computational Biology

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A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens.

PloS one
Identifying protective antigens (PAs), i.e., targets for bacterial vaccines, is challenging as conducting in-vivo tests at the proteome scale is impractical. Reverse Vaccinology (RV) aids in narrowing down the pool of candidates through computational...

Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images.

Computer methods and programs in biomedicine
OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients.

GNNMutation: a heterogeneous graph-based framework for cancer detection.

BMC bioinformatics
BACKGROUND: When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as in the interactions between proteins. These changes can disrupt cell function and contribute to the d...

HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.

BMC bioinformatics
BACKGROUND: Accurate identification of drug-drug interactions (DDIs) is critical in pharmacology, as DDIs can either enhance therapeutic efficacy or trigger adverse reactions when multiple medications are administered concurrently. Traditional method...

Efficient structure learning of gene regulatory networks with Bayesian active learning.

BMC bioinformatics
BACKGROUND: Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions. Bayesian causal discovery provides a principled framework for modeling observational d...

NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.

Interdisciplinary sciences, computational life sciences
Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI resear...

Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction.

Journal of biomedical informatics
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused...

READRetro web: A user-friendly platform for predicting plant natural product biosynthesis.

Molecules and cells
Natural products (NPs), a fundamental class of bioactive molecules with broad applicability, are valuable sources in pharmaceutical research and drug discovery. Despite their significance, the large-scale production of NPs is often limited by their a...

Exploration of the potential therapeutic effects and targets of Coriandrum sativum on non-erosive esophagitis based on bioinformatics and molecular dynamics simulation.

Scientific reports
Gastroesophageal reflux disease (GERD) is one of the most frequently diagnosed gastrointestinal disorders, adversely affecting quality of life. Coriandrum sativum has been shown to effectively promote gastrointestinal motility, alleviate gastric disc...

Prediction of Ligand-Receptor Interactions Based on CatBoost and Deep Forest and Their Application in Cell-Cell Communication Analysis.

Journal of chemical information and modeling
Cell-to-cell communication (CCC) is prominent for cell growth and development as well as tissue and organ formation. CCC inference can help us to deeply understand cellular interplay and discover potential therapeutic targets for complex diseases. Ce...