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genie3 matlab version  (MathWorks Inc)
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Maximum node degree for predicted GRNs of varying SNR on hub-prone data. Ten datasets were used for GRN inference with LSCON, LSCO, LASSO, RidgeCO and <t>Genie3.</t> The prediction was done on simulated data containing infinitesimal values and the network selected was the predicted GRN with a median degree closest to the median degree of the true GRN, from a set of 30 GRNs of varying sparsity generated for each dataset by each method. The simulated data contained 100 ( A ), 300 ( B ), 500 ( C ) or 800 ( D ) genes corresponding to the titles in the figure. The average maximum degree of the true GRNs is shown as a dotted line. The simulated data were generated from GRNs with scale-free topology
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Maximum node degree for predicted GRNs of varying SNR on hub-prone data. Ten datasets were used for GRN inference with LSCON, LSCO, LASSO, RidgeCO and Genie3. The prediction was done on simulated data containing infinitesimal values and the network selected was the predicted GRN with a median degree closest to the median degree of the true GRN, from a set of 30 GRNs of varying sparsity generated for each dataset by each method. The simulated data contained 100 ( A ), 300 ( B ), 500 ( C ) or 800 ( D ) genes corresponding to the titles in the figure. The average maximum degree of the true GRNs is shown as a dotted line. The simulated data were generated from GRNs with scale-free topology

Journal: Bioinformatics

Article Title: Fast and accurate gene regulatory network inference by normalized least squares regression

doi: 10.1093/bioinformatics/btac103

Figure Lengend Snippet: Maximum node degree for predicted GRNs of varying SNR on hub-prone data. Ten datasets were used for GRN inference with LSCON, LSCO, LASSO, RidgeCO and Genie3. The prediction was done on simulated data containing infinitesimal values and the network selected was the predicted GRN with a median degree closest to the median degree of the true GRN, from a set of 30 GRNs of varying sparsity generated for each dataset by each method. The simulated data contained 100 ( A ), 300 ( B ), 500 ( C ) or 800 ( D ) genes corresponding to the titles in the figure. The average maximum degree of the true GRNs is shown as a dotted line. The simulated data were generated from GRNs with scale-free topology

Article Snippet: The methods are LSCO, LASSO, ( Tjärnberg et al. , 2013 , ), ridge regression ( Friedman et al. , 2010 ) with cut-off (RidgeCO) and Genie3 ( Huynh-Thu et al. , 2010 ); note that the Matlab version of Genie3 was used here.

Techniques: Generated

Accuracy of GRNs predicted from hub-prone data. LSCON, LSCO, LASSO, RidgeCO and Genie3 were used to predict GRNs from 10 datasets of 100 ( A ), 300 ( B ) and 500 ( C ) genes, as well as from 5 datasets of 800 ( D ) genes, corresponding to the titles in the figure. The simulated data contained small singular values and was generated from GRNs with scale-free topology. The AUPR is plotted over signal to noise ratio (SNR) as the methods are expected to perform better at higher SNR levels

Journal: Bioinformatics

Article Title: Fast and accurate gene regulatory network inference by normalized least squares regression

doi: 10.1093/bioinformatics/btac103

Figure Lengend Snippet: Accuracy of GRNs predicted from hub-prone data. LSCON, LSCO, LASSO, RidgeCO and Genie3 were used to predict GRNs from 10 datasets of 100 ( A ), 300 ( B ) and 500 ( C ) genes, as well as from 5 datasets of 800 ( D ) genes, corresponding to the titles in the figure. The simulated data contained small singular values and was generated from GRNs with scale-free topology. The AUPR is plotted over signal to noise ratio (SNR) as the methods are expected to perform better at higher SNR levels

Article Snippet: The methods are LSCO, LASSO, ( Tjärnberg et al. , 2013 , ), ridge regression ( Friedman et al. , 2010 ) with cut-off (RidgeCO) and Genie3 ( Huynh-Thu et al. , 2010 ); note that the Matlab version of Genie3 was used here.

Techniques: Generated

Mean execution time for five GRNI methods. The methods LSCO, LSCON, LASSO, RidgeCO and Genie3 were run on simulated data of varying sizes and their execution time measured in CPU time. Ten datasets at each size were used for determining average runtime for each method. Due to the excessive runtime of Genie3, we could not run it on the 800 gene datasets as the total runtime exceeded 100 wall clock hours

Journal: Bioinformatics

Article Title: Fast and accurate gene regulatory network inference by normalized least squares regression

doi: 10.1093/bioinformatics/btac103

Figure Lengend Snippet: Mean execution time for five GRNI methods. The methods LSCO, LSCON, LASSO, RidgeCO and Genie3 were run on simulated data of varying sizes and their execution time measured in CPU time. Ten datasets at each size were used for determining average runtime for each method. Due to the excessive runtime of Genie3, we could not run it on the 800 gene datasets as the total runtime exceeded 100 wall clock hours

Article Snippet: The methods are LSCO, LASSO, ( Tjärnberg et al. , 2013 , ), ridge regression ( Friedman et al. , 2010 ) with cut-off (RidgeCO) and Genie3 ( Huynh-Thu et al. , 2010 ); note that the Matlab version of Genie3 was used here.

Techniques: