A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping

Xiaohui Chen, Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan Mills, Charles Lee, Jinbo Xu
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:208-217, 2012.

Abstract

Learning a small number of genetic variants associated with multiple complex genetic traits is of practical importance and remains challenging due to the high dimensional nature of data. In this paper, we proposed a two-graph guided multi-task Lasso to address this issue with an emphasis on estimating subnetwork-to-subnetwork associations in expression quantitative trait loci (eQTL) mapping. The proposed model can learn such subnetwork-to-subnetwork associations and therefore can be seen as a generalization of several state-of-the-art multi-task feature selection methods. Additionally, this model has a nice property of allowing flexible structured sparsity on both feature and label domains. Simulation study shows the improved performance of our model and a human eQTL data set is analyzed to further demonstrate the applications of the model.

Cite this Paper


BibTeX
@InProceedings{pmlr-v22-chen12b, title = {A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping}, author = {Xiaohui Chen and Xinghua Shi and Xing Xu and Zhiyong Wang and Ryan Mills and Charles Lee and Jinbo Xu}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {208--217}, year = {2012}, editor = {Neil D. Lawrence and Mark Girolami}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/chen12b/chen12b.pdf}, url = {http://proceedings.mlr.press/v22/chen12b.html}, abstract = {Learning a small number of genetic variants associated with multiple complex genetic traits is of practical importance and remains challenging due to the high dimensional nature of data. In this paper, we proposed a two-graph guided multi-task Lasso to address this issue with an emphasis on estimating subnetwork-to-subnetwork associations in expression quantitative trait loci (eQTL) mapping. The proposed model can learn such subnetwork-to-subnetwork associations and therefore can be seen as a generalization of several state-of-the-art multi-task feature selection methods. Additionally, this model has a nice property of allowing flexible structured sparsity on both feature and label domains. Simulation study shows the improved performance of our model and a human eQTL data set is analyzed to further demonstrate the applications of the model.} }
Endnote
%0 Conference Paper %T A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping %A Xiaohui Chen %A Xinghua Shi %A Xing Xu %A Zhiyong Wang %A Ryan Mills %A Charles Lee %A Jinbo Xu %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-chen12b %I PMLR %J Proceedings of Machine Learning Research %P 208--217 %U http://proceedings.mlr.press %V 22 %W PMLR %X Learning a small number of genetic variants associated with multiple complex genetic traits is of practical importance and remains challenging due to the high dimensional nature of data. In this paper, we proposed a two-graph guided multi-task Lasso to address this issue with an emphasis on estimating subnetwork-to-subnetwork associations in expression quantitative trait loci (eQTL) mapping. The proposed model can learn such subnetwork-to-subnetwork associations and therefore can be seen as a generalization of several state-of-the-art multi-task feature selection methods. Additionally, this model has a nice property of allowing flexible structured sparsity on both feature and label domains. Simulation study shows the improved performance of our model and a human eQTL data set is analyzed to further demonstrate the applications of the model.
RIS
TY - CPAPER TI - A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping AU - Xiaohui Chen AU - Xinghua Shi AU - Xing Xu AU - Zhiyong Wang AU - Ryan Mills AU - Charles Lee AU - Jinbo Xu BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics PY - 2012/03/21 DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-chen12b PB - PMLR SP - 208 DP - PMLR EP - 217 L1 - http://proceedings.mlr.press/v22/chen12b/chen12b.pdf UR - http://proceedings.mlr.press/v22/chen12b.html AB - Learning a small number of genetic variants associated with multiple complex genetic traits is of practical importance and remains challenging due to the high dimensional nature of data. In this paper, we proposed a two-graph guided multi-task Lasso to address this issue with an emphasis on estimating subnetwork-to-subnetwork associations in expression quantitative trait loci (eQTL) mapping. The proposed model can learn such subnetwork-to-subnetwork associations and therefore can be seen as a generalization of several state-of-the-art multi-task feature selection methods. Additionally, this model has a nice property of allowing flexible structured sparsity on both feature and label domains. Simulation study shows the improved performance of our model and a human eQTL data set is analyzed to further demonstrate the applications of the model. ER -
APA
Chen, X., Shi, X., Xu, X., Wang, Z., Mills, R., Lee, C. & Xu, J.. (2012). A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in PMLR 22:208-217

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