AI Medical Compendium Journal:
Bioinformatics (Oxford, England)

Showing 1 to 10 of 847 articles

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.

Bioinformatics (Oxford, England)
MOTIVATION: As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, a...

OntoBrowser: a collaborative tool for curation of ontologies by subject matter experts.

Bioinformatics (Oxford, England)
UNLABELLED: The lack of controlled terminology and ontology usage leads to incomplete search results and poor interoperability between databases. One of the major underlying challenges of data integration is curating data to adhere to controlled term...

Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibil...

Extensive complementarity between gene function prediction methods.

Bioinformatics (Oxford, England)
MOTIVATION: The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct ge...

Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates.

Bioinformatics (Oxford, England)
MOTIVATION: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that ...

MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood.

Integrated gene set analysis for microRNA studies.

Bioinformatics (Oxford, England)
MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limita...

NegGOA: negative GO annotations selection using ontology structure.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction met...

TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

Bioinformatics (Oxford, England)
MOTIVATION: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine le...

A knowledge-based approach for predicting gene-disease associations.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat de...