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Title:
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Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques
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Author:
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González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Gámez Moreno, María G.; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E.; López Morteo, Gabriel A.
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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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. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
-Facioscapulohumeral Muscular Dystrophy
-Machine learning
-Gene discovery
-Feature selection
-Protein-protein association networks
-Selection algorithms
-Classification
-Distròfia muscular facioescapulohumeral
-Aprenentatge automàtic |
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Rights:
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Document type:
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Article - Published version
Article |
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