Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques
| Autor: | Lluís A. Belanche-Muñoz, Gabriel Lopez-Morteo, Jorge E. Ibarra-Esquer, Felix F. Gonzalez-Navarro, María G. Gámez-Moreno, Brenda L. Flores-Rios |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. SOCO - Soft Computing |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
musculoskeletal diseases
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC] Gene discovery Facioscapulohumeral Muscular Dystrophy Biology Machine learning computer.software_genre Machine Learning Atrophy Aprenentatge automàtic Genetics medicine Facioscapulohumeral muscular dystrophy Humans Biological evidence Gene Regulatory Networks Genetic Predisposition to Disease Muscle Skeletal Molecular Biology Genetic Association Studies Protein-protein association networks business.industry Disease progression Muscle weakness General Medicine Progressive muscle weakness Selection algorithms medicine.disease Classification Muscular Dystrophy Facioscapulohumeral Gene Expression Regulation Protein Biosynthesis Mutation Feature selection Artificial intelligence medicine.symptom business computer Distròfia muscular facioescapulohumeral Gene Discovery Algorithms |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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