Literature DB >> 19850150

MeSHing molecular sequences and clinical trials: a feasibility study.

Elizabeth S Chen1, Indra Neil Sarkar.   

Abstract

The centralized and public availability of molecular sequence and clinical trial data presents an opportunity to identify potentially valuable linkages across the bench-to-bedside "T1" translational barrier. In this study, we sought to leverage keyword metadata (Medical Subject Heading [MeSH] descriptors) to infer relationships between molecular sequences and clinical trials, as indexed by GenBank and ClinicalTrials.gov. The results of this feasibility study found that approximately 30% of sequences in GenBank could be linked to trials and over 90% of trials in ClinicalTrials.gov could be linked to sequences through MeSH descriptors. In a cursory evaluation, we were able to consistently identify meaningful linkages between molecular sequences and clinical trials. Based on our findings, there may be promise in subsequent studies aiming to identify linkages across the T1 translational barrier using existing large repositories.

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Mesh:

Year:  2009        PMID: 19850150      PMCID: PMC2878930          DOI: 10.1016/j.jbi.2009.10.003

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  20 in total

1.  Better access to information about clinical trials.

Authors:  A T McCray
Journal:  Ann Intern Med       Date:  2000-10-17       Impact factor: 25.391

2.  Linking biomedical language information and knowledge resources: GO and UMLS.

Authors:  I N Sarkar; M N Cantor; R Gelman; F Hartel; Y A Lussier
Journal:  Pac Symp Biocomput       Date:  2003

3.  Exploring text mining from MEDLINE.

Authors:  Padmini Srinivasan; Thomas Rindflesch
Journal:  Proc AMIA Symp       Date:  2002

4.  Design, implementation and management of a web-based data entry system for ClinicalTrials.gov.

Authors:  John E Gillen; Tony Tse; Nicholas C Ide; Alexa T McCray
Journal:  Stud Health Technol Inform       Date:  2004

5.  Searching NCBI databases using Entrez.

Authors:  Andreas D Baxevanis
Journal:  Curr Protoc Bioinformatics       Date:  2008-12

6.  Rising expectations: access to biomedical information.

Authors:  D A B Lindberg; B L Humphreys
Journal:  Yearb Med Inform       Date:  2008

7.  The National Library of Medicine and medical informatics.

Authors:  D A Lindberg; H M Schoolman
Journal:  West J Med       Date:  1986-12

8.  Textpresso: an ontology-based information retrieval and extraction system for biological literature.

Authors:  Hans-Michael Müller; Eimear E Kenny; Paul W Sternberg
Journal:  PLoS Biol       Date:  2004-09-21       Impact factor: 8.029

9.  From phenotype to genotype: issues in navigating the available information resources.

Authors:  J A Mitchell; A T McCray; O Bodenreider
Journal:  Methods Inf Med       Date:  2003       Impact factor: 2.176

10.  GenBank.

Authors:  Dennis A Benson; Ilene Karsch-Mizrachi; David J Lipman; James Ostell; Eric W Sayers
Journal:  Nucleic Acids Res       Date:  2008-10-21       Impact factor: 16.971

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  3 in total

1.  Current methodologies for translational bioinformatics.

Authors:  Yves A Lussier; Atul J Butte; Lawrence Hunter
Journal:  J Biomed Inform       Date:  2010-05-12       Impact factor: 6.317

2.  A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.

Authors:  Tasnia Tahsin; Davy Weissenbacher; Robert Rivera; Rachel Beard; Mari Firago; Garrick Wallstrom; Matthew Scotch; Graciela Gonzalez
Journal:  J Am Med Inform Assoc       Date:  2016-01-17       Impact factor: 4.497

3.  GenBank as a Source to Monitor and Analyze Host-Microbiome Data.

Authors:  Vivek Ramanan; Shanti Mechery; Indra Neil Sarkar
Journal:  Bioinformatics       Date:  2022-07-08       Impact factor: 6.931

  3 in total

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