http://precedings.nature.com/tags/MeSH Recently posted documents tagged with 'MeSH' 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.2009.3775.1 Key assertions are extracted from “conclusions” sections of PubMed abstracts andconverted into Semantic Web / Linked Data format. The results are made accessible viafiles, a SPARQL endpoint, and a faceted search interface. Conclusion sections areidentified as valuable resources for machine-augmented key assertion identification andintegration in the biomedical domain. Results are discussed and opportunities for futurework and cooperation are highlighted. http://dx.doi.org/10.1038/npre.2009.3775.1 Mon, 21 Sep 2009 08:29:29 UTC Extracting conclusion sections from PubMed abstracts for rapid key assertion integration in biomedical research doi:10.1038/npre.2009.3775.1 2009-09-21 Nature Precedings 2009-09-21T08:29:29Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ 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://dx.doi.org/10.1038/npre.2007.425.3 Does your research area re-use shared datasets?* Re-using data has many benefits, including research synergy and efficient resource use* Some research areas have tools, communities, and practices which facilitate re-use* Identifying these areas will allow us to learn from them, and apply the lessons to areas which underutilize the sharing and re-purposing of scientific data between investigators Which datasets?This preliminary analysis examines the re-use of microarray gene expression datasets. Thousands of microarray gene expression datasets have been deposited in publicly available databases. Many studies reuse this data, but it is not well understood for what purposes. Here, we examined all publications found in PubMed Central on April 1, 2007 whose full-text contained the phrases “microarray” and “gene expression” to find studies which re-used microarray data. How did we identify re-use?We developed prototype machine-learning classifiers to identify a) studies containing original microarray data (n=900) and b) studies which instead re-used microarray data (n=250). Preprocessing (Python NLTK) extracted manually-selected keyword frequencies from the full-text publications as features for a Support Vector Machine (SVMlite). The classifier was trained and tested on a manually-labeled set of documents (PLoS articles prior to January 2007 containing the word “microarray,” n=200). How did we identify patterns of re-use?We compared the Medical Subject Heading (MeSH) of the two classes to estimate the odds that a specific MeSH term would be used given all studies with original microarray data, compared to the odds of the same term describing studies with re-used data. Terms were truncated to comparable levels in the MeSH hierarchy. ResultsPublications with original vs. re-used microarray data have different distributions of MeSH terms (Figure 1), and occur in different proportions across various journals (Figure 2). Microarray data source (original vs. re-used) did not affect the odds of a study focusing on humans, mice, or invertebrates, whereas publications with re-used data did involve a relatively high proportion of studies involving fungi (odds ratio (OR)=2.4), and a relatively low proportion involving rats, bacteria, viruses, plants, or genetically-altered or inbred animals (OR Trends in odds ratios of MeSH terms for other attributes can be seen in Figure 3. HopeAlthough not all research topics can be addressed by re-using existing data, many can. Identifying areas with frequent re-use can highlight best practices to be used when developing research agendas, tools, standards, repositories, and communities in areas which have yet to receive major benefits from shared data. Future WorkWe plan to refine our tool for identifying studies which re-use data, and continue studying and measuring re-use and reusability. http://dx.doi.org/10.1038/npre.2007.425.3 Wed, 18 Jul 2007 13:26:38 UTC Examining the uses of shared data doi:10.1038/npre.2007.425.3 2007-07-18 Heather A. Piwowar Nature Precedings 2007-07-18T13:26:38Z Poster Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.425.2 BackgroundMany initiatives and repositories exist to encourage the sharing of research data, and thousands of microarray gene expression datasets are publicly available. Many studies reuse this data, but it is not well understood which datasets are reused and for what purpose.Materials and MethodsWe trained a machine-learning algorithm to automatically classify full-text gene expression microarray studies into two classes: those that generated original microarray data (n=900) and those which only reused data (n=250). We then compared the Medical Subject Heading (MeSH) terms of two classes to identify MeSH topics which were over- or under-represented by publications with reused data.ResultsStudies on humans, mice, chordata, and invertebrates were equally likely to be conducted using original or shared microarray data, whereas shared data was used in a relatively high proportion of studies involving fungi (odds ratio (OR)=2.4), and a relatively low proportion involving rats, bacteria, viruses, plants, or genetically-altered or inbred animals (ORDiscussionIdentifying areas of particularly successful microarray data reuse—such as Saccharomyces cerevisiae datasets and studies of promoter regions and evolution—can highlight best practices to be used when developing research agendas, tools, standards, repositories, and communities in areas which have yet to receive major benefits from shared data. http://dx.doi.org/10.1038/npre.2007.425.2 Tue, 17 Jul 2007 13:56:37 UTC Examining the uses of shared data doi:10.1038/npre.2007.425.2 2007-07-17 Heather A. Piwowar Nature Precedings 2007-07-17T13:56:37Z Poster Bioinformatics http://creativecommons.org/licenses/by/2.5/