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      Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB

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      Author(s): , Ph.D 1 , , M.B.B.S., Ph.D 2 , , Ph.D 3
      Publication date (Electronic):
      Journal: Summit on Translational Bioinformatics
      Publisher: American Medical Informatics Association

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          Abstract

          Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt’s enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language.

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          A literature network of human genes for high-throughput analysis of gene expression.

          A Laegreid, E Hovig, T. Jenssen … (2001)
          We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets.
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            Is Open Access

            Extending the mutual information measure to rank inferred literature relationships

            Jonathan Wren (2004)
            Background Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scale-free networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations. Results Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology categories) found within MEDLINE are used to create a network of associations for evaluation. Conclusions Mutual information calculations can be effectively extended into implied relationships and a significance cutoff estimated from analysis of random word networks. Of the models tested, the shared minimum MIM (MMIM) model is found to correlate best with the observed strength and frequency of known associations. Using three test cases, the MMIM method tends to rank more specific relationships higher than counting the number of shared relationships within a network.
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              TransMiner: mining transitive associations among biological objects from text.

              Vijay Narayanasamy, Snehasis Mukhopadhyay, Mathew Palakal … (2015)
              Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that co-occur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine. 2004 National Science Council, ROC and S. Karger AG, Basel
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                Author and article information

                Journal
                Journal ID (nlm-ta): Summit on Translat Bioinforma
                Title: Summit on Translational Bioinformatics
                Publisher: American Medical Informatics Association
                ISSN (Electronic): 2153-6430
                Publication date Collection: 2009
                Publication date (Electronic): 1 March 2009
                Volume: 2009
                Pages: 124-128
                Affiliations
                [1 ]Departments of Computer Science,
                [2 ]Biomedical Informatics, and
                [3 ]Symbolic Systems (consulting) Stanford University, Stanford, CA 94305 USA
                Author notes
                Article
                Publisher ID: amia-s2009-124
                PMC ID: 3041559
                PubMed ID: 21347183
                SO-VID: 772a46df-3dd9-4742-88ec-18e007a0acee
                Copyright © ©2009 AMIA - All rights reserved.
                License:

                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose

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                ScienceOpen disciplines: Bioinformatics & Computational biology
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                ScienceOpen disciplines: Bioinformatics & Computational biology

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