Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

• Published in: Genes & Genetic Systems Vol. 90; no. 6; pp. 343 - 356
• Main Authors: González-Navarro, Félix F., Belanche-Muñoz, Lluís A., Gámez-Moreno, María G., Flores-Ríos, Brenda L., Ibarra-Esquer, Jorge E., López-Morteo, Gabriel A.
• Format: Journal Article Publication
• Language: English; Japanese
• Published: Japan The Genetics Society of Japan 01.12.2015; Japan Science and Technology Agency
• Subjects: Algorithms; Aplicacions de la informàtica; Aprenentatge automàtic; Bioinformàtica; Classification; Distròfia muscular facioescapulohumeral; Facioscapulohumeral Muscular Dystrophy; Feature selection; Gene discovery; Gene Expression Regulation; Gene Regulatory Networks - genetics; Genetic Association Studies; Genetic Predisposition to Disease; Humans; Informàtica; Machine Learning; Muscle, Skeletal - metabolism; Muscle, Skeletal - pathology; Muscular Dystrophy, Facioscapulohumeral - genetics; Muscular Dystrophy, Facioscapulohumeral - physiopathology; Mutation; Protein Biosynthesis - genetics; Protein-protein association networks; Selection algorithms; Àrees temàtiques de la UPC
• ISSN: 1341-7568; 1880-5779
• DOI: 10.1266/ggs.15-00017

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.