{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:44:18Z","timestamp":1768419858283,"version":"3.49.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T00:00:00Z","timestamp":1563753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"NIH\/NIDDK","award":["1R01DK107666-01"],"award-info":[{"award-number":["1R01DK107666-01"]}]},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-16-1-0516"],"award-info":[{"award-number":["W81XWH-16-1-0516"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["SBIR 1853207"],"award-info":[{"award-number":["SBIR 1853207"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher\u2019s method, Stouffer\u2019s method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer\u2019s disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz561","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T11:23:46Z","timestamp":1563535426000},"page":"487-495","source":"Crossref","is-referenced-by-count":16,"title":["GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis"],"prefix":"10.1093","volume":"36","author":[{"given":"Adib","family":"Shafi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Wayne State University , Detroit, MI 48202, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8001-9470","authenticated-orcid":false,"given":"Tin","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada , Reno, NV 89557, USA"}]},{"given":"Azam","family":"Peyvandipour","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University , Detroit, MI 48202, USA"}]},{"given":"Sorin","family":"Draghici","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University , Detroit, MI 48202, USA"},{"name":"Department of Obstetrics and Gynecology, Wayne State University , Detroit, MI 48202, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,7,22]]},"reference":[{"key":"2023020108351666000_btz561-B1","first-page":"D562","article-title":"NCBI GEO: mining millions of expression profiles\u2013database and tools","volume":"33(Database Issue)","author":"Barrett","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023020108351666000_btz561-B2","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.3233\/JAD-141635","article-title":"The role of endocannabinoid signaling in the molecular mechanisms of neurodegeneration in Alzheimer\u2019s disease","volume":"43","author":"Bedse","year":"2015","journal-title":"J. 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