Multi-population GWA mapping via multi-task regularized regression
نویسندگان
چکیده
منابع مشابه
Multi-population GWA mapping via multi-task regularized regression
MOTIVATION Population heterogeneity through admixing of different founder populations can produce spurious associations in genome-wide association studies that are linked to the population structure rather than the phenotype. Since samples from the same population generally co-evolve, different populations may or may not share the same genetic underpinnings for the seemingly common phenotype. O...
متن کاملManifold Regularized Multi-Task Learning
Multi-task learning (MTL) has drawn a lot of attentions in machine learning. By training multiple tasks simultaneously, information can be better shared across tasks. This leads to significant performance improvement in many problems. However, most existing methods assume that all tasks are related or their relationship follows a simple and specified structure. In this paper, we propose a novel...
متن کاملFactorisable Multi-Task Quantile Regression∗
We propose a multivariate quantile regression framework that exploits the factor structure in multivariate conditional quantiles through nuclear norm regularization. Because the incurred optimization problem can only be solved approximately, we develop a non-asymptotic upper bound for the estimation error that takes into account the optimization error. We specify an algorithm to compute an appr...
متن کاملSemi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method ca...
متن کاملMulti-task Regression using Minimal Penalties Multi-task Regression using Minimal Penalties
In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this problem shows that the key element appearing for an optimal calibration is the covariance matrix of the noise between the different tasks. We present a new algorithm to estimate this covariance matrix, based on the con...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2010
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btq191