AI Medical Compendium Journal:
Bioinformatics (Oxford, England)

Showing 61 to 70 of 847 articles

SimSon: simple contrastive learning of SMILES for molecular property prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles...

Automatic biomarker discovery and enrichment with BRAD.

Bioinformatics (Oxford, England)
MOTIVATION: Integrating Large Language Models (LLMs) with research tools presents technical and reproducibility challenges for biomedical research. While commercial artificial intelligence (AI) systems are easy to adopt, they obscure data provenance,...

OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem.

Bioinformatics (Oxford, England)
SUMMARY: The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently, there has be...

High-dimensional biomarker identification for interpretable disease prediction via machine learning models.

Bioinformatics (Oxford, England)
MOTIVATION: Omics features, often measured by high-throughput technologies, combined with clinical features, significantly impact the understanding of many complex human diseases. Integrating key omics biomarkers with clinical risk factors is essenti...

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

Bioinformatics (Oxford, England)
SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challen...

ProtNote: a multimodal method for protein-function annotation.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, <1% of known protein sequences have human-verified functions. While deep-learning methods have demonstrated prom...

Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcit...

scMUSCL: multi-source transfer learning for clustering scRNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis relies heavily on effective clustering to facilitate numerous downstream applications. Although several machine learning methods have been developed to enhance single-cell clustering, most a...

EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Enhancers and promoters, as regulatory DNA elements, play pivotal roles in gene expression, homeostasis, and disease development across various biological processes. With advancing research, it has been uncovered that distal enhancers may...

MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been w...