Abstract
A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.
Original language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
Editors | Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 431-438 |
Number of pages | 8 |
ISBN (Electronic) | 9781509030491 |
DOIs | |
State | Published - Dec 15 2017 |
Externally published | Yes |
Event | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017 |
Other
Other | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
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Country/Territory | United States |
City | Kansas City |
Period | 11/13/17 → 11/16/17 |
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics