ArticleBMC bioinformatics2009
PhenoGO: an integrated resource for the multiscale mining of clinical and biological data.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations.Human genetics · 2021Article
- Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies.Computers in biology and medicine · 2018Article
- CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.Scientific reports · 2017Article
- Integrated analysis of the potential roles of miRNA‑mRNA networks in triple negative breast cancer.Molecular medicine reports · 2017Article
- The landscape of microbial phenotypic traits and associated genes.Nucleic acids research · 2016Article
- Integrated miRNA profiling and bioinformatics analyses reveal potential causative miRNAs in gastric adenocarcinoma.Oncotarget · 2015Article
- Event-based text mining for biology and functional genomics.Briefings in functional genomics · 2015Review
- COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Toward knowledge support for analysis and interpretation of complex traits.Genome biology · 2013Review
- Computational approaches for translational clinical research in disease progression.Journal of investigative medicine : the official publication of the American Federation for Clinical Research · 2011Review
- MicroRNAs involved in the mitogen-activated protein kinase cascades pathway during glucose-induced cardiomyocyte hypertrophy.The American journal of pathology · 2011Article
- Discovering disease associations by integrating electronic clinical data and medical literature.PloS one · 2011Article
- Evaluation of an Ontology-anchored Natural Language-based Approach for Asserting Multi-scale Biomolecular Networks for Systems Medicine.Summit on translational bioinformatics · 2010Article
- Between proteins and phenotypes: annotation and interpretation of mutations.BMC bioinformatics · 2009Article
- Linking genes to diseases: it's all in the data.Genome medicine · 2009Article
- Selected proceedings of the First Summit on Translational Bioinformatics 2008.BMC bioinformatics · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.