%0 Journal Article %1 Sinha2009 %A Sinha, Anshu %A Hripcsak, George %A Markatou, Marianthi %D 2009 %J Journal of the American Medical Informatics Association : JAMIA %K BiomedicalResearch BiomedicalResearch:statistics&numericaldata DataInterpretation Databases Factual Humans InformationManagement InterdisciplinaryCommunication Models Statistical %N 6 %P 759-67 %R 10.1197/jamia.M2780 %T Large datasets in biomedicine: a discussion of salient analytic issues. %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3002128&tool=pmcentrez&rendertype=abstract %V 16 %X Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference to inform a principled and generalizable analysis and highlight the interdisciplinary nature of these challenges. The authors present a detailed study of germane issues including high dimensionality, multiple testing, scientific significance, dependence, information measurement, and information management with a focus on appropriate methodologies available to address these concerns. A firm understanding of the challenges and statistical technology involved ultimately contributes to better science. The authors further suggest that the community consider facilitating discussion through interdisciplinary panels, invited papers and curriculum enhancement to establish guidelines for analysis and reporting. %@ 1527-974X; 1067-5027

@article{Sinha2009, abstract = {Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference to inform a principled and generalizable analysis and highlight the interdisciplinary nature of these challenges. The authors present a detailed study of germane issues including high dimensionality, multiple testing, scientific significance, dependence, information measurement, and information management with a focus on appropriate methodologies available to address these concerns. A firm understanding of the challenges and statistical technology involved ultimately contributes to better science. The authors further suggest that the community consider facilitating discussion through interdisciplinary panels, invited papers and curriculum enhancement to establish guidelines for analysis and reporting.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Sinha, Anshu and Hripcsak, George and Markatou, Marianthi}, biburl = {https://www.bibsonomy.org/bibtex/2d6826a0cca165bb31617ed86fe21cc78/jepcastel}, city = {Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.}, doi = {10.1197/jamia.M2780}, interhash = {0da7106a6c7088bb10c5cea7847d2846}, intrahash = {d6826a0cca165bb31617ed86fe21cc78}, isbn = {1527-974X; 1067-5027}, issn = {1527-974X}, journal = {Journal of the American Medical Informatics Association : JAMIA}, keywords = {BiomedicalResearch BiomedicalResearch:statistics&numericaldata DataInterpretation Databases Factual Humans InformationManagement InterdisciplinaryCommunication Models Statistical}, note = {5502<m:linebreak></m:linebreak>GR: R01 LM06910/LM/NLM NIH HHS/United States; GR: T15 LM007079-17/LM/NLM NIH HHS/United States; JID: 9430800; 2009/08/28 [aheadofprint]; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Epidemiologia genètica; Sample size; Comparacions múltiples; Introductori}, number = 6, pages = {759-67}, pmid = {19717808}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Large datasets in biomedicine: a discussion of salient analytic issues.}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3002128&tool=pmcentrez&rendertype=abstract}, volume = 16, year = 2009 }