Graph-Sparse Logistic Regression
This addresses classification tasks in bioinformatics, specifically proteomics, where graph connectivity is important, but it appears incremental as it builds on existing logistic regression methods.
The authors tackled the problem of classification when the support should be sparse but connected on a graph, introducing Graph-Sparse Logistic Regression and validating it against synthetic data and L1-regularized Logistic Regression, with application to proteomics data on an interactome graph.
We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experimental code public and provide GSLR as an open source package.