ArticleBioMed research international2016
Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease.International journal of molecular sciences · 2024Article
- Tissue registration and exploration user interfaces in support of a human reference atlas.Communications biology · 2022Article
- Finding Gene Associations by Text Mining and Annotating it with Gene Ontology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.BMC genomics · 2018Article
- Article
- Multiple kernels learning-based biological entity relationship extraction method.Journal of biomedical semantics · 2017Article
- Investigations on factors influencing HPO-based semantic similarity calculation.Journal of biomedical semantics · 2017Article
- BRWLDA: bi-random walks for predicting lncRNA-disease associations.Oncotarget · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
- Applying Precision Public Health to Prevent Preterm Birth.Frontiers in public health · 2017Review
- Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules.BioMed research international · 2017Article
- Long Non-coding RNAs in Hepatitis C Virus-Infected Cells.Frontiers in microbiology · 2017Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401, p = 2.2e - 16) and gene frequency of individual term (Pearson correlation = 0.1298, p = 3.686e - 14) in GeneRIFs and GOA shows our annotation resource is very reliable.
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