PLSNET statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.

Subscribe
info

Citations per year

Citations chart
info

Popular tool citations

chevron_left Gene regulatory network inference chevron_right
Popular tools chart
info

Tool usage distribution map

Tool usage distribution map
info

Associated diseases

Associated diseases

PLSNET specifications

Information


Unique identifier OMICS_14585
Name PLSNET
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Gene expression measurements for p genes in experimental conditions.
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes

Download


Versioning


Add your version

Maintainer


  • person_outline Donghui Guo <>

Publication for PLSNET

PLSNET in publications

 (2)
PMCID: 4856318
PMID: 27145341
DOI: 10.1371/journal.pcbi.1004888

[…] t-test p-values [, , ]. 3) learning a condition-specific conditional dependency network for each condition and comparing the networks between conditions: gill et al. (2010) proposed a method, called plsnet, that fits a partial least squares model to each gene, computes a connectivity scores between genes, and then calculates the l1 distance between score vectors to estimate network perturbation […]

PMCID: 4771175
PMID: 26928298
DOI: 10.1371/journal.pcbi.1004765

[…] considered an outlier compared with the other partial correlation methods. therefore, if we remove it as a separate group, the remaining partial correlation methods ridgenet, lassonet, elasticnet, plsnet, glasso, genenet can also be categorized as ‘regularized methods’., in the pca plots, the 1st principal component (pc) primarily separates the correlation-based methods spearmancor […]


To access a full list of publications, you will need to upgrade to our premium service.

PLSNET institution(s)
Department of Electronic Engineering, Xiamen University, Fujian, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; School of Mathematics and Computer Science, Fujian Normal University, Fujian, China
PLSNET funding source(s)
This research was supported by Research Fund for National Natural Science Foundation of China (General Program) under Grant No. 61274133 and Shenzhen Technology Development Foundation Grant No. CXZZ20150813155917544.

PLSNET reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review PLSNET