Christopher A Mancuso

The paper introduces ModGenePlexus, a network-based gene classification method that improves disease gene discovery by decomposing gene lists into modules and combining module-specific classifiers.

GenePlexusZoo improves gene classification by integrating molecular networks from multiple species into a shared feature space, enhancing both single-species and cross-species predictions.

PyGenePlexus predicts gene associations with input gene sets using network-based machine learning and provides interpretability through comparison with known gene sets.

The paper presents a computational approach to identify gene signatures of chronic inflammation in complex diseases and prioritize drugs for new therapeutic opportunities.

This paper introduces SampleLASSO, a method for imputing unmeasured gene expression in microarray data, showing it outperforms existing methods across multiple tasks.

This study benchmarks supervised-learning for network-based gene classification, showing it outperforms label-propagation and node-embeddings in accuracy.