A mixture model with logistic weights for disease subtyping with integrated genome association study

Abstract : This work proposes an original method for disease subtyping from both longitudinal clinical variables and genetic markers via a mixture of regressions model, with logistic weights function of a potentially large number of genetic variables. In order to address these large-scale problems, variable selection is an essential step. We thus propose to discard genetic variables that may not be relevant for clustering by maximizing a penalized likelihood via a Classification Expectation Maximization algorithm. The proposed method is validated on simulations. The approach is applied to a data set from a cohort of Parkinson's disease patients. Several subtypes of the disease as well as genetic variants potentially having a role in this typology have been identified.
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https://hal.archives-ouvertes.fr/hal-01822237
Contributor : Marie Courbariaux <>
Submitted on : Friday, July 6, 2018 - 8:11:01 PM
Last modification on : Monday, April 29, 2019 - 5:32:48 PM
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  • HAL Id : hal-01822237, version 2

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Marie Courbariaux, Christophe Ambroise, Cyril Dalmasso, Marie Szafranski, Memodeep Consortium. A mixture model with logistic weights for disease subtyping with integrated genome association study. 2018. ⟨hal-01822237v2⟩

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