Graph-Sparse Logistic Regression

TL;DR

This paper presents Graph-Sparse Logistic Regression, an algorithm that enforces support sparsity and connectivity on a graph, validated on synthetic and proteomics data, with open-source implementation.

Contribution

It introduces a novel graph-structured sparsity constraint for logistic regression, bridging support sparsity and connectivity, with validation on synthetic and biological data.

Findings

GSLR outperforms L1-regularized logistic regression on synthetic data.

GSLR effectively identifies connected sparse supports in proteomics data.

The method is publicly available as an open-source package.

Abstract

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.