ArticleBMC bioinformatics2013
STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A longitudinal analysis of function annotations of the human proteome reveals consistently high biases.Database : the journal of biological databases and curation · 2025Article
- Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?Medical research archives · 2024Article
- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.Journal of clinical and translational science · 2024Article
- How do clinical researchers generate data-driven scientific hypotheses? Cognitive events using think-aloud protocol.medRxiv : the preprint server for health sciences · 2023Article
- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.medRxiv : the preprint server for health sciences · 2023Article
- HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.Journal of biomedical informatics · 2019Article
- iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data.BMC bioinformatics · 2018Article
- Inferring ontology graph structures using OWL reasoning.BMC bioinformatics · 2018Article
- Article
- A new approach for investigating venom function applied to venom calreticulin in a parasitoid wasp.Toxicon : official journal of the International Society on Toxinology · 2015Article
- Biomedical imaging ontologies: A survey and proposal for future work.Journal of pathology informatics · 2015Article
- HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the human phenotype ontology.PloS one · 2015Article
- Genome-wide DNA methylation changes with age in disease-free human skeletal muscle.Aging cell · 2014Article
- Thematic series on biomedical ontologies in JBMS: challenges and new directions.Journal of biomedical semantics · 2014Review
- Biases in the experimental annotations of protein function and their effect on our understanding of protein function space.PLoS computational biology · 2013Article
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Authors and funding
8 authors.
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Abstract
backgroundGene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.
resultsAs a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms.
conclusionMultiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/.
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