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• W377801081 abstract "Motivation: Identification and elucidation of eQTL has long been an active area of research. Finding cis-eQTL has been a manageable problem because of the limited number of candidates. Finding transeQTL has on the other hand been much more challenging because of the issue of multiple hypothesis testing. It has been suggested that additional information might alleviate this problem and although there has been some success using such methods no comprehensive data integration strategy has been developed. Approach: In order to comprehensively solve the issue of multiple hypothesis testing in the context of trans-eQTL discovery this research introduces MASSQTL: A comprehensive data integration method that makes use of a deep neural network (DNN) to prune the transeQTL candidate space to a desired size with the objective of finding more significantly associated trans-eQTL. Results: With MASSQTL many more trans-eQTL were found using a deep neural network filtering approach. The deep neural network outperformed other machine learning models showing that deep learning by use of complex hierarchical representations is able to model a diverse and sparse set of biological data. In addition to that the method provided new insight into the mechanisms underlying the regulatory architecture of gene expression." @default.
• W377801081 created "2016-06-24" @default.
• W377801081 creator A5023300922 @default.
• W377801081 date "2014-11-03" @default.
• W377801081 modified "2023-09-26" @default.
• W377801081 title "Identification and Elucidation of Expression Quantitative Trait Loci (eQTL) and their regulating mechanisms using Decodive Deep Learning" @default.
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• W377801081 hasPublicationYear "2014" @default.
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