Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

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dc.contributor.author Zeng, Sui en
dc.contributor.author Lumley, Thomas en
dc.date.accessioned 2018-10-03T22:25:55Z en
dc.date.issued 2018-02-20 en
dc.identifier.issn 1177-9322 en
dc.identifier.uri http://hdl.handle.net/2292/38638 en
dc.description.abstract Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix. en
dc.publisher Libertas Academica en
dc.relation.ispartofseries Bioinformatics and Biology Insights en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/1177-9322/ en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri http://www.creativecommons.org/licenses/by-nc/4.0/ en
dc.title Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science) en
dc.type Journal Article en
dc.identifier.doi 10.1177/1177932218759292 en
pubs.begin-page 1 en
pubs.volume 12 en
dc.rights.holder Copyright: The authors en
pubs.author-url http://journals.sagepub.com/toc/bbia/current%E2%80%8B en
pubs.end-page 16 en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Review en
pubs.elements-id 726165 en
pubs.org-id Science en
pubs.org-id Statistics en
dc.identifier.eissn 1177-9322 en
pubs.record-created-at-source-date 2018-02-23 en
pubs.online-publication-date 2018-02-20 en


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http://www.creativecommons.org/licenses/by-nc/4.0/ Except where otherwise noted, this item's license is described as http://www.creativecommons.org/licenses/by-nc/4.0/

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