title: Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.
abstract: In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.
authors: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany. inke.koenig@imbs.uni-luebeck.de.
authors: Inke R
authors: IR
authors: König
authors: Department of Statistics, Columbia University, New York, NY, 10027, USA. jla2167@columbia.edu.
authors: Jonathan
authors: J
authors: Auerbach
authors: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany. gola@imbs.uni-luebeck.de.
authors: Damian
authors: D
authors: Gola
authors: Department of Mathematics, Iowa State University, Ames, IA, 50011, USA. elheld@iastate.edu.
authors: Elizabeth
authors: E
authors: Held
authors: Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, 21224, USA. emily.holzinger@nih.gov.
authors: Emily R
authors: ER
authors: Holzinger
authors: Université de Montréal, Faculty of Medicine, 2900 Chemin de la Tour, Montreal, QC, H3T 1N8, Canada. marc-andre.legault.1@umontreal.ca.
authors: Marc-André
authors: MA
authors: Legault
authors: Division of Biostatistics, School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, Hong Kong SAR. rsunzju@gmail.com.
authors: Rui
authors: R
authors: Sun
authors: Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA, 51250, USA. nathan.tintle@dordt.edu.
authors: Nathan
authors: N
authors: Tintle
authors: Institute of Statistical Science, Academia Sinica, Nankang 115, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw.
authors: Hsin-Chou
authors: HC
authors: Yang
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D005820:Genetic Testing; D023281:Genomics / Q000379:methods*; D006801:Humans; D000069550:Machine Learning