http://precedings.nature.com/tags/GoPubMed Recently posted documents tagged with 'GoPubMed' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3122.1 Manual curation of biomedical data is highly accurate but time consuming, and does not scale with the ever increasing growth of biomedical literature. Text mining as a high-throughput computational technique scales well but requires human expertise to produce highly accurate results. Ontologies can help organizing large quantities of unstructured information. Here we present three systems, namely GoGene, GoPubMed and GoWeb, employing biomedical ontologies and show how they can assist manual curation of biomedical data.GoGene associates all genes from different model organisms to concepts of the Gene Ontology (GO) and the Medical Subject Headings (MeSH). The hierarchical structures of both terminologies support clustering and summarizing long lists of genes. Through the integration of known gene annotations from UniProt and EntrezGene with text-mined annotations from all abstracts in PubMed, GoGene currently contains up to 4,000,000 associations between genes and concepts from GO and MeSH for ten model organisms. The quality of all associations can be verified by following the links to their origin, that is, literature or database entries.GoPubMed aims at reducing the limitations of classical keyword search. It handles inconsistent vocabulary such as synonyms and specialized terminology. It shows the most relevant concepts in GO and MeSH for a search and thus reveals information which otherwise remains buried in the masses of text. This feature as well as the entire bibliography of all authors in PubMed facilitate comprehensive literature search. GoWeb translates these ideas to the World Wide Web and is thus not only limited to PubMed abstracts. GoWeb uses a standard web-search service and organizes search results based on GO, MeSH, and other concepts such as companies and institutions. http://dx.doi.org/10.1038/npre.2009.3122.1 Wed, 22 Apr 2009 21:14:18 UTC Ontology-based Assisted Curation of Biomedical Data doi:10.1038/npre.2009.3122.1 2009-04-22 Conrad Plake Nature Precedings 2009-04-22T21:14:18Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2101/version/2 With increasing number of bio-literature search engines, scientists and health professionals either make a subjective choice of tool(s) or face a challenge of analyzing multiple features of a plethora of bibliographic software. There is an urgent need for a thorough comparative analysis of the available literature scanning tools, from the user’s perspective. We report results of the first time semi-quantitative comparison of 21 search programs, which can search published (partial or full text) documents in life science areas. The observations can assist life science researchers and medical professionals to make an informed selection among the programs, depending on their search objectives. Some of the important findings are: 1. Most of the hits obtained from Scopus, ReleMed, EBImed, CiteXplore, and HighWire Press were usually relevant (i.e., these tools showed a better precision than other tools). 2. But a very high number of relevant citations were retrieved by HighWire Press, Google Scholar, CiteXplore and Pubmed Central (they had better recall). 3. HWP and CiteXplore seemed to have a good balance of precision and recall efficiencies. 4. PubMed Central, PubMed and Scopus provided the most useful query systems. 5. GoPubMed, BioAsk, EBIMed, ClusterMed could be more useful among the tools that can automatically process the retrieved citations for further scanning of bio-entities such as proteins, diseases, tissues, molecular interactions etc). The authors suggest the use of PubMed, Scopus, Google Scholar and HighWire Press – for better coverage, and GoPubMed – to view the hits categorized based on the MeSH and gene ontology terms. http://precedings.nature.com/documents/2101/version/2 Thu, 24 Jul 2008 15:38:26 UTC How do you choose your literature search tool(s)? hdl:10101/npre.2008.2101.2 2008-07-24 Kshitish K. Acharya Nature Precedings 2008-07-24T15:38:26Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2101/version/1 With increasing number of bibliographic software, scientists and health professionals either make a subjective choice of tool(s) that could suit their needs or face a challenge of analyzing multiple features of a plethora of search programs. There is an urgent need for a thorough comparative analysis of the available bio-literature scanning tools, from the user’s perspective. We report results of the first time semi-quantitative comparison of 21 programs, which can search published (partial or full text) documents in life science areas. The observations can assist life science researchers and medical professionals to make an informed selection among the programs, depending on their search objectives. Some of the important findings are: 1. Most of the hits obtained from Scopus, ReleMed, EBImed, CiteXplore, and HighWire Press were usually relevant (i.e. these tools show a better precision than other tools). 2. But a very high number of relevant citations were retrieved by HighWire Press, Google Scholar, CiteXplore and Pubmed Central (they had better recall). 3. HWP and CiteXplore seemed to have a good balance of precision and recall efficiencies. 4. PubMed Central, PubMed and Scopus provided the most useful query systems. 5. GoPubMed, BioAsk, EBIMed, ClusterMed could be more useful among the tools that can automatically process the retrieved citations for further scanning of bio-entities such as proteins, diseases, tissues, molecular interactions, etc. The authors suggest the use of PubMed, Scopus, Google Scholar and HighWire Press – for better coverage, and GoPubMed – to view the hits categorized based on the MeSH and gene ontology terms. The article is relavant to all life science subjects. http://precedings.nature.com/documents/2101/version/1 Tue, 22 Jul 2008 10:50:58 UTC A comparative analysis of 21 literature search engines hdl:10101/npre.2008.2101.1 2008-07-22 Kshitish K. Acharya Nature Precedings 2008-07-22T10:50:58Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/