Russell Greiner

The paper develops deep-learning models to predict glycan-protein binding strengths using hybrid datasets and tail-aware losses, finding that glycan interactions are harder than small-molecule interactions due to chiral feature limitations.

This paper develops MCNet, a machine learning model that predicts protein-glycan binding strength using atom-level representations, enabling cross-chiral recognition and unifying diverse glycobiology datasets.

BioTransformer4.0 predicts small molecule metabolites using integrated knowledge-based and machine learning approaches, achieving high recall on benchmark datasets.

This paper introduces DeepMet, a chemical language model that predicts and discovers unknown metabolites using mass spectrometry data.

This paper introduces a Reproducibility Score to assess biomarker set reproducibility and provides methods to estimate it using univariate hypothesis testing on dichotomous groups.

GlyNet predicts protein-glycan interactions with continuous relative binding strengths, outperforming classifiers and serving as a benchmark for quantitative glycobiology.

This paper presents a crowdsourced challenge to predict early discontinuation of docetaxel in mCRPC patients using clinical data from multiple trials, finding that ensemble models outperform individual models.