کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10356116 | 867614 | 2011 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
NCBIGEOFTPEBIUMLSNLMMEDLINEAnnotations - حاشیه نویسیUnified Medical Language System - سیستم زبان یونیکسPride - غرورNational Center for Biotechnology Information - مرکز ملی اطلاعات بیوتکنولوژیMesh - مشEuropean Bioinformatics Institute - موسسه بیوانفورماتیک اروپاMedical Subject Headings - موضوعات موضوعی پزشکیOntologies - هستی شناسیNatural Language Processing - پردازش زبانهای طبیعیProteomics - پروتئومیکسfile transfer protocol - پروتکل انتقال فایلGene Expression Omnibus - ژن بیان OmnibusNational Library of Medicine - کتابخانه ملی پزشکی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Publicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as “gold standard”. Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Biomedical Informatics - Volume 44, Supplement 1, December 2011, Pages S39-S43
Journal: Journal of Biomedical Informatics - Volume 44, Supplement 1, December 2011, Pages S39-S43
نویسندگان
David Ruau, Michael Mbagwu, Joel T. Dudley, Vijay Krishnan, Atul J. Butte,