Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression.

ABSTRACT: Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of trea

SUBMITTER: Gal O 

PROVIDER: S-EPMC6423478 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature