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Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

journal contribution

posted on 2016-02-01, 00:00 authored by Joanna Frith Dipnall, Julie PascoJulie Pasco, Michael BerkMichael Berk, Lana WilliamsLana Williams, Seetal DoddSeetal Dodd, Felice JackaFelice Jacka, D Meyer

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

History

Journal

PLoS ONE

Volume

11

Issue

2

Article number

ARTN e0148195

Publisher

PUBLIC LIBRARY SCIENCE

Location

United States

Publisher DOI

https://doi.org/10.1371/journal.pone.0148195

ISSN

1932-6203

eISSN

1932-6203

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000369554000050&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2016, The Authors

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Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

Research Publications

Categories

Uncategorized

Keywords

Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics CELL DISTRIBUTION WIDTH MULTIPLE IMPUTATION REGRESSION CLASSIFICATION BILIRUBIN RISK ANTIOXIDANT DISEASE INFLAMMATION DIAGNOSIS

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All Rights Reserved

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