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10.1093/bioinformatics/btu293 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu293 C4058913!4058913 !24931977     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24931977 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (12 ): i139-48 Nephropedia Template TP {{tp|p=24931977 |t=2014. Graph-regularized dual Lasso for robust eQTL mapping |pdf=|usr=}}{{24931977 }} gab.com Text Graph-regularized dual Lasso for robust eQTL mapping JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931977 #FOAvip MOTIVATION: As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated. RESULTS: To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods. AVAILABILITY: Software for both C++ version and Matlab version is available at http://www.cs.unc.edu/?weicheng/. Twit Text FOAVip Graph-regularized dual Lasso for robust eQTL mapping ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931977 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931977 #FOAvip English Wikipedia <ref>{{Cite pmid|24931977 }}</ref> Graph-regularized dual Lasso for robust eQTL mapping #MMPMID24931977 Cheng W ; Zhang X ; Guo Z ; Shi Y ; Wang W Bioinformatics 2014[Jun]; 30 (12 ): i139-48 PMID24931977 show ga MOTIVATION: As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated. RESULTS: To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods. AVAILABILITY: Software for both C++ version and Matlab version is available at http://www.cs.unc.edu/?weicheng/. |*Gene Expression [MESH] |*Polymorphism, Single Nucleotide [MESH] |*Quantitative Trait Loci [MESH] |Algorithms [MESH] |Chromosome Mapping [MESH] |Cluster Analysis [MESH] |Gene Regulatory Networks [MESH] |Genetic Markers [MESH] |Linear Models [MESH] |Protein Interaction Mapping [MESH]Graph-regularized dual Lasso for robust eQTL mapping (epmc).pdf DeepDyve Pubget Overpricing