ArticleGenome medicine2012
Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles.
Article in Genome medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
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- The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs.Science advances · 2020Article
- Oral lichen planus interactome reveals CXCR4 and CXCL12 as candidate therapeutic targets.Scientific reports · 2020Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- Altered learning, memory, and social behavior in type 1 taste receptor subunit 3 knock-out mice are associated with neuronal dysfunction.The Journal of biological chemistry · 2017Article
- MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank.Journal of biomedical semantics · 2017Article
- Beegle: from literature mining to disease-gene discovery.Nucleic acids research · 2016Article
- Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types.Genome medicine · 2015Article
- Hippocampal Transcriptomic and Proteomic Alterations in the BTBR Mouse Model of Autism Spectrum Disorder.Frontiers in physiology · 2015Article
- GIT2 Acts as a Systems-Level Coordinator of Neurometabolic Activity and Pathophysiological Aging.Frontiers in endocrinology · 2015Article
- Identification of highly related references about gene-disease association.BMC bioinformatics · 2014Article
- A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms.BMC bioinformatics · 2014Article
- Associating disease-related genetic variants in intergenic regions to the genes they impact.PeerJ · 2014Article
- Assessing identity, redundancy and confounds in Gene Ontology annotations over time.Bioinformatics (Oxford, England) · 2013Article
- Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity.BMC medical genomics · 2013Article
- Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles.Genome medicine · 2012Article
- Mining the literature: new methods to exploit keyword profiles.Genome medicine · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundMEDLINE(®)/PubMed(®) currently indexes over 18 million biomedical articles, providing unprecedented opportunities and challenges for text analysis. Using Medical Subject Heading Over-representation Profiles (MeSHOPs), an entity of interest can be robustly summarized, quantitatively identifying associated biomedical terms and predicting novel indirect associations.
methodsA procedure is introduced for quantitative comparison of MeSHOPs derived from a group of MEDLINE(®) articles for a biomedical topic (for example, articles for a specific gene or disease). Similarity scores are computed to compare MeSHOPs of genes and diseases.
resultsSimilarity scores successfully infer novel associations between diseases and genes. The number of papers addressing a gene or disease has a strong influence on predicted associations, revealing an important bias for gene-disease relationship prediction. Predictions derived from comparisons of MeSHOPs achieves a mean 8% AUC improvement in the identification of gene-disease relationships compared to gene-independent baseline properties.
conclusionsMeSHOP comparisons are demonstrated to provide predictive capacity for novel relationships between genes and human diseases. We demonstrate the impact of literature bias on the performance of gene-disease prediction methods. MeSHOPs provide a rich source of annotation to facilitate relationship discovery in biomedical informatics.
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.