Abstract
Genome-wide association (GWA) studies have recently become popular as a tool for identifying genetic variables that are responsible for increased disease susceptibility. A modern statistical method for approaching this problem is through model selection (or structure estimation) of Structured Input-Output Regression Models (SIORM) fitted on genetic and phenotypic variation data across a large number of individuals.
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© 2011 Springer-Verlag Berlin Heidelberg
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Xing, E. (2011). Genome-Phenome Association Analysis of Complex Diseases a Structured Sparse Regression Approach. In: Chen, J., Wang, J., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2011. Lecture Notes in Computer Science(), vol 6674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21260-4_5
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DOI: https://doi.org/10.1007/978-3-642-21260-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21259-8
Online ISBN: 978-3-642-21260-4
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