Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Arika Fukushima, Masahiro Sugimoto, Satoru Hiwa, Tomoyuki Hiroyasu

ABSTRACT

INF-β has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-β treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-β treatment for MS patients. More... »

PAGES

1822

References to SciGraph publications

  • 2012-12. Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data in BMC NEUROLOGY
  • 2017-12. Matrix metalloproteinase activity stimulates N-cadherin shedding and the soluble N-cadherin ectodomain promotes classical microglial activation in JOURNAL OF NEUROINFLAMMATION
  • 2016-12. Weakly supervised learning of biomedical information extraction from curated data in BMC BIOINFORMATICS
  • 2002-04. New concepts in the immunopathogenesis of multiple sclerosis in NATURE REVIEWS NEUROSCIENCE
  • 2018-12. Feature selection for high-dimensional temporal data in BMC BIOINFORMATICS
  • 2012-08. Studying and modelling dynamic biological processes using time-series gene expression data in NATURE REVIEWS GENETICS
  • 2015-08. Predicting disease progression from short biomarker series using expert advice algorithm in SCIENTIFIC REPORTS
  • 2015-12. Discovering monotonic stemness marker genes from time-series stem cell microarray data in BMC GENOMICS
  • 2008-12. The prediction of interferon treatment effects based on time series microarray gene expression profiles in JOURNAL OF TRANSLATIONAL MEDICINE
  • 2012-04. Network analysis of transcriptional regulation in response to intramuscular interferon-β-1a multiple sclerosis treatment in THE PHARMACOGENOMICS JOURNAL
  • 2007-03. Significance analysis of microarray transcript levels in time series experiments in BMC BIOINFORMATICS
  • 2009-12. Recursive regularization for inferring gene networks from time-course gene expression profiles in BMC SYSTEMS BIOLOGY
  • 2012-12. Elevated type I interferon-like activity in a subset of multiple sclerosis patients: molecular basis and clinical relevance in JOURNAL OF NEUROINFLAMMATION
  • Journal

    TITLE

    Scientific Reports

    ISSUE

    1

    VOLUME

    9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-38441-2

    DOI

    http://dx.doi.org/10.1038/s41598-018-38441-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112066518

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30755676


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