What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?

Christina Vasilopoulou, Andrew P. Morris, George Giannakopoulos, Stephanie Duguez, William Duddy

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)
5 Downloads (Pure)

Abstract

Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
Original languageEnglish
Article number247
Pages (from-to)1-28
Number of pages28
JournalJournal of Personalized Medicine
Volume10
Issue number4
Early online date26 Nov 2020
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Amyotrophic Lateral Sclerosis
  • machine learning
  • genome-wide association studies
  • GWAS
  • genomics
  • ALS pathology
  • gene prioritization

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