Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.

Journal: BMC genetics
Published Date: Sep 17, 2018

Abstract

BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 to integrate genome-wide genotype and methylation array data.

Authors

  • Burcu Darst
    Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, 610 Walnut St. 1007 WARF, Madison, WI, 53726, USA.
  • Corinne D Engelman
    Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, 610 Walnut St. 1007 WARF, Madison, WI, 53726, USA.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Justo Lorenzo Bermejo
    Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany. lorenzo@imbi.uni-heidelberg.de.

Keywords

External Resources

Popular Topics
Recent Journals