AI Medical Compendium Topic:
Gene Ontology

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Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

Bioinformatics (Oxford, England)
MOTIVATION: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena ...

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.

Bioinformatics (Oxford, England)
MOTIVATION: Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the fact...

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontol...

pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England)
MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence informat...

SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.

Bioinformatics (Oxford, England)
MOTIVATION: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolate...

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data.

The Review of scientific instruments
Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is...

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Bioinformatics (Oxford, England)
MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often ...

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

Circulation. Genomic and precision medicine
BACKGROUND: A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene On...

Gramene 2018: unifying comparative genomics and pathway resources for plant research.

Nucleic acids research
Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,...