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Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Titel:	Reviewing and assessing existing meta-analysis models and tools.
Autor/in / Beteiligte Person:	Makinde, FL ; Tchamga, MSS ; Jafali, J ; Fatumo, S ; Chimusa, ER ; Mulder, N ; Mazandu, GK
Link:	Full Text Finder (Volltext)
Zeitschrift:	Briefings in bioinformatics, Jg. 22 (2021-11-05), Heft 6
Veröffentlichung:	Oxford : Oxford University Press ; <i>Original Publication</i>: London ; Birmingham, AL : H. Stewart Publications, [2000-, 2021
Medientyp:	academicJournal
ISSN:	1477-4054 (electronic)
DOI:	10.1093/bib/bbab324
Schlagwort:	Decision Trees Humans Workflow Algorithms Data Analysis Meta-Analysis as Topic Software
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review Language: English [Brief Bioinform] 2021 Nov 05; Vol. 22 (6). MeSH Terms: Algorithms* ; Data Analysis* ; Meta-Analysis as Topic* ; Software* ; Decision Trees ; Humans ; Workflow References: Control Clin Trials. 1986 Sep;7(3):177-88. (PMID: 3802833) ; Infect Immun. 2012 Mar;80(3):1150-5. (PMID: 22232187) ; Am J Hum Genet. 2011 May 13;88(5):586-98. (PMID: 21565292) ; Nat Commun. 2019 Aug 14;10(1):3669. (PMID: 31413261) ; Stat Med. 1999 Feb 15;18(3):321-59. (PMID: 10070677) ; Bioinformatics. 2020 Jan 15;36(2):524-532. (PMID: 31359040) ; Phytopathology. 2011 Jan;101(1):31-41. (PMID: 20854109) ; BMC Bioinformatics. 2008 Feb 28;9:130. (PMID: 18307795) ; BMC Bioinformatics. 2010 May 28;11:288. (PMID: 20509871) ; PLoS One. 2007 Sep 05;2(9):e841. (PMID: 17786212) ; EBioMedicine. 2019 Feb;40:614-625. (PMID: 30638864) ; FEBS Lett. 2004 Aug 27;573(1-3):83-92. (PMID: 15327980) ; J Immunol. 2007 Jul 1;179(1):557-65. (PMID: 17579077) ; Nucleic Acids Res. 2012 May;40(9):3785-99. (PMID: 22262733) ; Nat Rev Genet. 2013 Jun;14(6):379-89. (PMID: 23657481) ; Infect Immun. 2006 Oct;74(10):5561-73. (PMID: 16988231) ; Biostatistics. 2007 Jan;8(1):118-27. (PMID: 16632515) ; Methods Mol Biol. 2016;1418:161-76. (PMID: 27008014) ; Sci Am. 1949 May;180(5):11-5. (PMID: 18138375) ; Brief Bioinform. 2021 Jul 20;22(4):. (PMID: 33129201) ; BMC Bioinformatics. 2013 Dec 21;14:368. (PMID: 24359104) ; Ann Appl Stat. 2014;8(2):777-800. (PMID: 25383132) ; BMC Med Genomics. 2019 Jan 9;12(1):3. (PMID: 30626383) ; Am J Epidemiol. 2009 Nov 15;170(10):1197-206. (PMID: 19808636) ; Mol Cancer. 2009 Sep 04;8:71. (PMID: 19732454) ; Methods Mol Biol. 2016;1418:93-110. (PMID: 27008011) ; Front Genet. 2021 Mar 15;12:595702. (PMID: 33790942) ; Bioinformatics. 2008 Feb 1;24(3):374-82. (PMID: 18204063) ; Psychol Bull. 1951 May;48(3):156-8. (PMID: 14834286) ; Bioinformatics. 2003;19 Suppl 1:i84-90. (PMID: 12855442) ; Adv Bioinformatics. 2011;2011:801478. (PMID: 22190924) ; Front Med (Lausanne). 2019 Mar 01;6:34. (PMID: 30881956) ; Cancer Cell. 2006 Feb;9(2):121-32. (PMID: 16473279) ; BMC Med Res Methodol. 2018 May 21;18(1):44. (PMID: 29783954) ; Brief Bioinform. 2021 Mar 22;22(2):1694-1705. (PMID: 32095826) ; Bioinformatics. 2010 Sep 1;26(17):2190-1. (PMID: 20616382) ; Hum Mol Genet. 2013 Jun 1;22(11):2303-11. (PMID: 23406875) ; BMC Bioinformatics. 2010 Aug 03;11:408. (PMID: 20678237) ; Bioinformatics. 2019 May 1;35(9):1597-1599. (PMID: 30304367) ; Anticancer Res. 2014 Mar;34(3):1355-66. (PMID: 24596383) ; J Sport Health Sci. 2020 Dec;9(6):511-520. (PMID: 32745617) ; Genet Epidemiol. 2011 Dec;35(8):809-22. (PMID: 22125221) ; Bioinformatics. 2006 Nov 15;22(22):2825-7. (PMID: 16982708) ; Nucleic Acids Res. 2012 May;40(9):3777-84. (PMID: 22241776) ; Bioinformatics. 2012 Oct 1;28(19):2534-6. (PMID: 22863766) ; PLoS One. 2012;7(5):e38347. (PMID: 22675457) ; Brief Bioinform. 2019 Mar 25;20(2):690-700. (PMID: 29701762) ; PLoS Genet. 2012;8(3):e1002555. (PMID: 22396665) Grant Information: U24 HG006941 United States HG NHGRI NIH HHS; U24 HL135600 United States HL NHLBI NIH HHS Contributed Indexing: Keywords: cohort study; data integration; experimental study; meta-analysis; predictive power; sample size Entry Date(s): Date Created: 20210820 Date Completed: 20220309 Latest Revision: 20231108 Update Code: 20240513 PubMed Central ID: PMC8575034

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Reviewing and assessing existing meta-analysis models and tools.