LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data

181 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Large-scale sparse logistic regression (2009, background): “ing document classification [4, 5], computer vision [9], natural language processing [14, 22], and bioinformatics [2, 20, 29 , 34, 35].”
Message passing neural networks for molecular property prediction (2019, methodology): “However, more complicated techniques could be used in the future to fit to empirical CDFs, such as finding the best fit general logistic function, which has been shown to be successful for other biological datasets [38].”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “These biases inflate enrichment results and can result in false positives, so it is important to identify appropriate control sets or to apply the correct statistical methods (permutation tests or covariate modelling 187 ).”