Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Journal: Computers in biology and medicine

PMID: 31627017

Abstract

BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

Authors

Enrique J deAndrés-Galiana

1 Artificial Intelligence Center, Universidad de Oviedo , Asturias, Spain .
Guillermina Bea

Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/ Federico García Lorca, 18 33007, Oviedo, Asturias, Spain. Electronic address: jlfm@uniovi.es.
Juan L Fernández-Martínez

Symptom Management Branch, Division of Intramural Research, National Institute of Nursing Research, Building 3, Room 5E14 3 Center Drive Bethesda, MD 20892, USA. Electronic address: guillermina.bea@icloud.com.
Leo N Saligan

Symptom Management Branch, Division of Intramural Research, National Institute of Nursing Research, Building 3, Room 5E14 3 Center Drive Bethesda, MD 20892, USA. Electronic address: saliganl@mail.nih.gov.

Keywords

Databases, Nucleic Acid Drug Repositioning Female Gene Expression Regulation Humans Machine Learning Male Metabolic Networks and Pathways Models, Immunological Multiple Sclerosis Oligonucleotide Array Sequence Analysis Retrospective Studies Signal Transduction

External Resources

View on PubMed Access via DOI PubMed (31627017)

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Abstract

Authors

Keywords

External Resources

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