Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data.

ABSTRACT: Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least

SUBMITTER: Akter S 

PROVIDER: S-EPMC6737999 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature