Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning.

ABSTRACT:

Background

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

Methods

We pe

SUBMITTER: Founta K 

PROVIDER: S-EPMC9872307 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature