Su-In Lee

Computer ScienceBiochemistry, Genetics and Molecular Biology

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 37 papers. Top paper: “Forecasting adverse surgical events using self-supervised transfer learning for physiological signals”.

Forecasting adverse surgical events using self-supervised transfer learning for physiological signals

npj Digital Medicine(2021)10.1038/s41746-021-00536-y

N/A

2.1DataRank · unranked

51 citations

Scott Lundberg, Gabriel Erion, Jerry H. Kim, Su-In Lee, Hugh Chen

From local explanations to global understanding with explainable AI for trees

Nature Machine Intelligence(2020)10.1038/s42256-019-0138-9

N/A

1.4DataRank · unranked

8346 citations

Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair +8 more

Reproducibility standards for machine learning in the life sciences

Nature Methods(2021)10.1038/s41592-021-01256-7

N/A

0.808DataRank · unranked

218 citations

Michael M. Hoffman, Jeffrey R. Marks, Su-In Lee, Casey S. Greene, Stephanie C. Hicks +1 more

Interpretable machine learning prediction of all-cause mortality

Communications Medicine(2022)10.1038/s43856-022-00180-x

N/A

0.697DataRank · unranked

103 citations

Hugh Chen, Ayse B. Dincer, Scott Lundberg, Matt Kaeberlein, Su-In Lee +1 more

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age

The Lancet Healthy Longevity(2023)10.1016/s2666-7568(23)00189-7

N/A

0.650DataRank · unranked

75 citations

Hugh Chen, Matt Kaeberlein, Su-In Lee, Wei Qiu

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.02.26.967208

N/A

0.425DataRank · unranked

16 citations

Christine A. Petersen, Joanne C. Wen, Michael R. Banitt, Philip Chen, Karine D. Bojikian +6 more

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies

Nature Communications(2021)10.1038/s41467-021-25680-7

N/A

0DataRank · unranked

19 citations

Safiye Çelik, Ethan Weinberger, Pascal Sturmfels, Philip L. De Jager, Sara Mostafavi +2 more

Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Nature Machine Intelligence(2021)10.1038/s42256-021-00343-w

N/A

0DataRank · unranked

22 citations

Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, Su-In Lee, Gabriel Erion

Course Corrections for Clinical AI

Kidney360(2021)10.34067/kid.0004152021

N/A

0DataRank · unranked

1 citations

Joseph D. Janizek, Su-In Lee, Alex J. DeGrave

CoAI: Cost-Aware Artificial Intelligence for Health Care

medRxiv(2021)10.1101/2021.01.19.21249356

N/A

0DataRank · unranked

4 citations

Joseph D. Janizek, Carly Hudelson, Richard B. Utarnachitt, Andrew M. McCoy, Michael R. Sayre +3 more

Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study

Zenodo (CERN European Organization for Nuclear Research)(2021)10.5281/zenodo.6339664

N/A

0DataRank · unranked

0 citations

Alex Okeson, Tim Althoff, Su-In Lee, Nicasia Beebe-Wang

Explaining a series of models by propagating Shapley values

Nature Communications(2022)10.1038/s41467-022-31384-3

N/A

0DataRank · unranked

248 citations

Scott Lundberg, Su-In Lee, Hugh Chen

A cost-aware framework for the development of AI models for healthcare applications

Nature Biomedical Engineering(2022)10.1038/s41551-022-00872-8

N/A

0DataRank · unranked

40 citations

Joseph D. Janizek, Carly Hudelson, Richard B. Utarnachitt, Andrew M. McCoy, Michael R. Sayre +3 more

An automatic integrative method for learning interpretable communities of biological pathways

NAR Genomics and Bioinformatics(2022)10.1093/nargab/lqac044

N/A

0DataRank · unranked

3 citations

Ayse B. Dincer, Su-In Lee, Nicasia Beebe-Wang

Algorithms to estimate Shapley value feature attributions

arXiv (Cornell University)(2022)10.48550/arxiv.2207.07605

N/A

0DataRank · unranked

21 citations

Ian Covert, Scott Lundberg, Su-In Lee, Hugh Chen

Interpretable Machine Learning Prediction of All-Cause Mortality

Research Square(2022)10.21203/rs.3.rs-1352145/v1

N/A

0DataRank · unranked

4 citations

Hugh Chen, Ayşe Dinçer, Scott Lundberg, Matt Kaeberlein, Su-In Lee +1 more

Predictive and robust gene selection for spatial transcriptomics

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.05.13.491738

N/A

0DataRank · unranked

4 citations

Rohan Gala, Tim Wang, Karel Svoboda, Uygar Sümbül, Su-In Lee +1 more

Algorithms to estimate Shapley value feature attributions

Nature Machine Intelligence(2023)10.1038/s42256-023-00657-x

N/A

0DataRank · unranked

427 citations

Ian Covert, Scott Lundberg, Su-In Lee, Hugh Chen

Isolating salient variations of interest in single-cell data with contrastiveVI

Nature Methods(2023)10.1038/s41592-023-01955-3

N/A

0DataRank · unranked

64 citations

Chris Lin, Su-In Lee, Ethan Weinberger

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

Nature Biomedical Engineering(2023)10.1038/s41551-023-01160-9

N/A

0DataRank · unranked

50 citations

Zhuo Ran Cai, Joseph D. Janizek, Roxana Daneshjou, Su-In Lee, Alex J. DeGrave

Predictive and robust gene selection for spatial transcriptomics

Nature Communications(2023)10.1038/s41467-023-37392-1

N/A

0DataRank · unranked

39 citations

Rohan Gala, Tim Wang, Karel Svoboda, Uygar Sümbül, Su-In Lee +1 more

PAUSE: principled feature attribution for unsupervised gene expression analysis

Genome biology(2023)10.1186/s13059-023-02901-4

N/A

0DataRank · unranked

25 citations

Anna Spiro, Safiye Çelik, Ben W. Blue, John Russell, Ting-I Lee +3 more

Dissection of medical AI reasoning processes via physician and generative-AI collaboration

medRxiv(2023)10.1101/2023.05.12.23289878

N/A

0DataRank · unranked

14 citations

Zhuo Ran Cai, Joseph D. Janizek, Roxana Daneshjou, Su-In Lee, Alex J. DeGrave

Fostering transparent medical image AI via an image-text foundation model grounded in medical literature

medRxiv(2023)10.1101/2023.06.07.23291119

N/A

0DataRank · unranked

14 citations

Soham Gadgil, Alex J. DeGrave, Zhuo Ran Cai, Roxana Daneshjou, Su-In Lee +1 more

Learning to Maximize Mutual Information for Dynamic Feature Selection

arXiv (Cornell University)(2023)10.48550/arxiv.2301.00557

N/A

0DataRank · unranked

6 citations

Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee +1 more

On the Robustness of Removal-Based Feature Attributions

arXiv (Cornell University)(2023)10.48550/arxiv.2306.07462

N/A

0DataRank · unranked

2 citations

Ian Covert, Su-In Lee, Chris Lin

Estimating Conditional Mutual Information for Dynamic Feature Selection

arXiv (Cornell University)(2023)10.48550/arxiv.2306.03301

N/A

0DataRank · unranked

0 citations

Ian Covert, Su-In Lee, Soham Gadgil

Feature Selection in the Contrastive Analysis Setting

arXiv (Cornell University)(2023)10.48550/arxiv.2310.18531

N/A

0DataRank · unranked

0 citations

Ian Covert, Su-In Lee, Ethan Weinberger

Transparent medical image AI via an image–text foundation model grounded in medical literature

Nature Medicine(2024)10.1038/s41591-024-02887-x

N/A

0DataRank · unranked

88 citations

Soham Gadgil, Alex J. DeGrave, Jesutofunmi A. Omiye, Zhuo Ran Cai, Roxana Daneshjou +2 more

The clinical potential of counterfactual AI models

The Lancet(2024)10.1016/s0140-6736(24)00313-1

N/A

0DataRank · unranked

10 citations

Eric J. Topol, Su-In Lee

A deep profile of gene expression across 18 human cancers

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.03.17.585426

N/A

0DataRank · unranked

3 citations

Ayse B. Dincer, Joseph D. Janizek, Safiye Çelik, Mikaël J. Pittet, Kamila Naxerova +2 more

Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution

arXiv (Cornell University)(2024)10.48550/arxiv.2401.15866

N/A

0DataRank · unranked

1 citations

Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto, Ian Covert

Adversarial deconfounding autoencoder for learning robust gene expression embeddings

Bioinformatics(2020)10.1093/bioinformatics/btaa796

N/A

0DataRank · unranked

87 citations

Joseph D. Janizek, Su-In Lee, Ayse B. Dincer

Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.04.28.065052

N/A

0DataRank · unranked

5 citations

Joseph D. Janizek, Su-In Lee, Ayse B. Dincer

AI for radiographic COVID-19 detection selects shortcuts over signal

Nature Machine Intelligence(2021)10.1038/s42256-021-00338-7

N/A

0DataRank · unranked

486 citations

Joseph D. Janizek, Su-In Lee, Alex J. DeGrave

Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images

American Journal of Ophthalmology(2021)10.1016/j.ajo.2021.04.021

N/A

0DataRank · unranked

111 citations

Christine A. Petersen, Joanne C. Wen, Michael R. Banitt, Philip Chen, Karine D. Bojikian +6 more

Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study

IEEE Journal of Biomedical and Health Informatics(2021)10.1109/jbhi.2021.3059563

N/A

0DataRank · unranked

23 citations

Alex Okeson, Tim Althoff, Su-In Lee, Nicasia Beebe-Wang