Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression
Version 3 2024-06-17, 17:30
Version 2 2024-06-06, 09:25
Version 1 2016-03-09, 14:20
journal contribution
posted on 2024-06-17, 17:30 authored by JF Dipnall, Julie PascoJulie Pasco, Michael BerkMichael Berk, Lana WilliamsLana Williams, Seetal DoddSeetal Dodd, Felice JackaFelice Jacka, D MeyerFusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression
History
Journal
PLoS ONEVolume
11Season
Article Number : e0148195Article number
ARTN e0148195Location
United StatesOpen access
- Yes
ISSN
1932-6203eISSN
1932-6203Language
EnglishPublication classification
C Journal article, C1 Refereed article in a scholarly journalCopyright notice
2016, The AuthorsIssue
2Publisher
PUBLIC LIBRARY SCIENCEUsage metrics
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No categories selectedKeywords
Science & TechnologyMultidisciplinary SciencesScience & Technology - Other TopicsCELL DISTRIBUTION WIDTHMULTIPLE IMPUTATIONREGRESSIONCLASSIFICATIONBILIRUBINRISKANTIOXIDANTDISEASEINFLAMMATIONDIAGNOSIS110319 Psychiatry (incl Psychotherapy)920410 Mental HealthFaculty of HealthSchool of MedicineInnovation in Mental and Physical Health and Clinical TreatmentMD Multidisciplinary3202 Clinical sciences420313 Mental health services200409 Mental health
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