Adaptive Multi-Task Lasso: with Application to eQTL Detection

@inproceedings{Lee2010AdaptiveML,
  title={Adaptive Multi-Task Lasso: with Application to eQTL Detection},
  author={Seunghak Lee and Jun Zhu and Eric P. Xing},
  booktitle={NIPS},
  year={2010}
}
To understand the relationship between genomic variations among population and complex diseases, it is essential to detect eQTLs which are a ssociated with phenotypic effects. However, detecting eQTLs remains a challe nge due to complex underlying mechanisms and the very large number of genetic l oc involved compared to the number of samples. Thus, to address the problem, it is desirable to take advantage of the structure of the data and prior informa tion bout genomic locations such as… CONTINUE READING
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 248 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 56 extracted citations

Advanced Data Mining and Applications

Lecture Notes in Computer Science • 2017
View 10 Excerpts
Highly Influenced

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

AISTATS • 2012
View 5 Excerpts
Highly Influenced

Annealed Sparsity via Adaptive and Dynamic Shrinking

KDD • 2016
View 4 Excerpts
Highly Influenced

249 Citations

0204060'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 249 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 26 references

PLINK: a tool set for whole-genome association and population-based linkage analyses.

American journal of human genetics • 2007
View 4 Excerpts
Highly Influenced

Learning a Prior on Regulatory Potential from eQTL Data

PLoS genetics • 2009
View 4 Excerpts
Highly Influenced

The bayesian Lasso. Journal of the American Statistical Association

T. Park, G. Casella
2008
View 4 Excerpts
Highly Influenced

The bayesian Lasso

M. Schmidt B. M. Marlin
Journal of the American Statistical Association • 2008

Similar Papers

Loading similar papers…