Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects.

ABSTRACT: Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study r

SUBMITTER: Karthik KV 

PROVIDER: S-EPMC9878829 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature