Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

Article English OPEN
Dipnall, Joanna F.; Pasco, Julie A.; Berk, Michael; Williams, Lana J.; Dodd, Seetal; Jacka, Felice N.; Meyer, Denny;
(2016)
  • Publisher: Public Library of Science (PLoS)
  • Journal: PLoS ONE,volume 11,issue 2 (issn: 1932-6203, eissn: 1932-6203)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.1371/journal.pone.0148195, pmc: PMC4744063
  • Subject: Red Blood Cells | Applied Mathematics | Algorithms | Research Article | Information Technology | Anatomy | Mathematics | Data Mining | Artificial Intelligence | Mental Health and Psychiatry | Chemical Elements | Simulation and Modeling | Physical Sciences | Machine Learning Algorithms | Chemistry | Cellular Types | Biology and Life Sciences | Computer and Information Sciences | Biomarkers | Neuroscience | Mood Disorders | Research and Analysis Methods | Physiology | Blood Cells | Animal Cells | Medicine | Body Fluids | Machine Learning | Blood | Bile | Q | Hematology | R | Cell Biology | Biochemistry | Science | Bilirubin | Cadmium | Medicine and Health Sciences | Depression | Cognitive Science

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data a... View more