A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.

ABSTRACT:

Background

Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets.

Method

A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into group

SUBMITTER: Thomaidis GV 

PROVIDER: S-EPMC10668096 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature