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GFlasso specifications


Unique identifier OMICS_04406
Name GFlasso
Alternative name Graph-guided Fused lasso
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C++
Computer skills Advanced
Stability Stable
Maintained Yes


No version available

Publication for Graph-guided Fused lasso

GFlasso citations


Integrative regression network for genomic association study

BMC Med Genomics
PMCID: 4989890
PMID: 27535739
DOI: 10.1186/s12920-016-0192-7

[…] r datasets. A smaller MSE implies better performance.Fig. 3Because of its structural information in consideration of SIOL, this method significantly outperformed all other regression methods, whereas GFLasso, SGL, and Lasso tend to produce comparable results, while Lasso, which uses no structural information, produces the largest MSE. The overall performance in terms of MSE in decreasing order was […]


Penalized regression approaches to testing for quantitative trait rare variant association

Front Genet
PMCID: 4026747
PMID: 24860593
DOI: 10.3389/fgene.2014.00121

[…] iants to reduce the DF as in the Sum test, while reducing the downward bias of the parameter estimates based on an L1-type penalty. We compare the TLP-S and TLP-SG to the Lasso and graph-fused Lasso (gflasso) (Kim and Xing, ). The gflasso also pursues parameter grouping with an L1-penalty. Specifically, the gflasso shrinks two variants' effect sizes toward each other by penalizing their difference […]


Learning Gene Networks under SNP Perturbations Using eQTL Datasets

PLoS Comput Biol
PMCID: 3937098
PMID: 24586125
DOI: 10.1371/journal.pcbi.1003420

[…] on the smaller datasets, we were unable to compare the performance of MRCE on these larger simulated datasets. Instead, we compared our method with other computationally efficient methods, including GFlasso and a base-line approach of applying graphical lasso and lasso sequentially to learn gene networks and eQTLs.We use precision-recall curves and prediction errors as quantitative measures of […]


Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta analytic Approaches

PLoS Genet
PMCID: 3681686
PMID: 23785294
DOI: 10.1371/journal.pgen.1003491

[…] always less powerful than FE and the recently developed RE.There are a few other methods that attempt to detect eQTLs from the multiple tissue data such as Sparse Bayesian Multiple Regression and the GFlasso approach proposed by Petretto et al. and Kim et al. However, a key difference between these methods and Meta-Tissue is that they attempt to detect multiple variants (“multi-locus”) associate […]


Bridging the Gap between Genotype and Phenotype via Network Approaches

Front Genet
PMCID: 3668153
PMID: 23755063
DOI: 10.3389/fgene.2012.00227

[…] ove approaches constructed modules using expression and genomic profile without taking advantage of interdependence between the data. In contrast, Kim and Xing proposed a statistical framework called graph-guided fused lasso (GFlasso) for QTL(Quantitative Trait Locus) analysis to identify genetic variations associated with multiple correlated traits simultaneously (Kim and Xing, ). They first cons […]


Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

BMC Genomics
PMCID: 3616858
PMID: 23514438
DOI: 10.1186/1471-2164-14-196

[…] GFlasso [] takes a gene-interaction network as input, along with the genotype and gene-expression data, and performs a correlated association analysis to identify genomic regions that perturb correlat […]


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GFlasso institution(s)
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

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