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Nature Communications|August 3, 2022
Explaining a series of models by propagating Shapley valuesHugh Chen, Scott M Lundberg, Su-In LeeNPJ Digital Medicine|December 9, 2021
Forecasting adverse surgical events using self-supervised transfer learning for physiological signalsHugh Chen, Scott M Lundberg, Gabriel Erion, et al.Nucleic Acids Research|March 15, 2019
AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identificationNaozumi Hiranuma, Scott M Lundberg, Su-In LeeNature Machine Intelligence|July 2, 2020
From Local Explanations to Global Understanding with Explainable AI for TreesScott M Lundberg, Gabriel Erion, Hugh Chen, et al.The Lancet. Healthy Longevity|November 9, 2023
ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological ageWei Qiu, Hugh Chen, Matt Kaeberlein, et al.Genome Biology|May 4, 2016
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq dataScott M Lundberg, William B Tu, Brian Raught, et al.Communications Medicine|October 7, 2022
Interpretable machine learning prediction of all-cause mortalityWei Qiu, Hugh Chen, Ayse Berceste Dincer, et al.Nature Biomedical Engineering|May 1, 2023
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning modelsJoseph D Janizek, Ayse B Dincer, Safiye Celik, et al.Genome Biology|November 13, 2003
Application of independent component analysis to microarraysSu-In Lee, Serafim BatzoglouJournal of Microbiology and Biotechnology|April 21, 2026
Optimization of Cultivation Parameters for Scale-Up Production of Streptomyces recifensis SN1E1Su In Lee, Youn-Sig KwakPageof 10