Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach.

Bianca Wollenhaupt-AguiarDiego Librenza-GarciaGiovana BristotLaura PrzybylskiLaura StertzRenan Kubiachi BurqueKeila Mendes CeresérLucas SpanembergMarco Antônio CaldieraroBenicio N FreyMarcelo P FleckMarcia Kauer-Sant'AnnaIves Cavalcante PassosFlavio Kapczinski

Published in: The Australian and New Zealand journal of psychiatry (2019)

Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

Keyphrases

machine learning
big data
peripheral blood
clinical practice
electronic health record
artificial intelligence
genome wide
high resolution
depressive symptoms
deep learning
current status
bipolar disorder
gene expression
dna methylation
data analysis
mass spectrometry