Tibshirani, Robert

Genetics of 35 blood and urine biomarkers in the UK Biobank

Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., et al. (2021) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 744

A framework for feature selection in clustering

Witten, D. M., & Tibshirani, R. (2010) · Journal of the American Statistical Association

Statistical Methods and InferenceAdvanced Clustering Algorithms Research

Cited by 696

Empirical Bayes methods and false discovery rates for microarrays

Efron, B., & Tibshirani, R. (2002) · Genetic Epidemiology

Gene expression and cancer classificationStatistical Methods in Clinical Trials

Cited by 684

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., Tibshirani, R., Hastie, T., Alpert, A., Cui, L., et al. (2021) · Nature Aging

Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

Cited by 572

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns

Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology

Gene expression and cancer classificationBioinformatics and Genomic Networks

Cited by 547

A significance test for the lasso

Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics

Statistical Methods and InferenceAdvanced Statistical Methods and Models

Cited by 541

Transparency and reproducibility in artificial intelligence

Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 540

Defining the features and duration of antibody responses to SARS-CoV-2 infection associated with disease severity and outcome

Röltgen, K., Powell, A. E., Wirz, O. F., Stevens, B. A., Hogan, C. A., Najeeb, J., Hunter, M., Wang, H., Sahoo, M. K., Huang, C., et al. (2020) · Science Immunology

SARS-CoV-2 and COVID-19 ResearchCOVID-19 Clinical Research Studies

Cited by 452

Statistical learning and selective inference

Taylor, J., & Tibshirani, R. J. (2015) · Proceedings of the National Academy of Sciences

Statistical Methods and InferenceStatistical Methods and Applications

Cited by 398

Normalization, testing, and false discovery rate estimation for RNA-sequencing data

Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 369

Human transcriptome array for high-throughput clinical studies

Xu, W., Seok, J., Mindrinos, M. N., Schweitzer, A. C., Jiang, H., Wilhelmy, J., Clark, T. A., Kapur, K., Xing, Y., Faham, M., et al. (2011) · Proceedings of the National Academy of Sciences

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 124

Bootstrap confidence intervals and bootstrap approximations

Diciccio, T., & Tibshirani, R. (1987) · Journal of the American Statistical Association

Statistical Methods and InferenceStatistical Distribution Estimation and Applications

Cited by 95

Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service

Callahan, A., Gombar, S., Cahan, E. M., Jung, K., Steinberg, E., Polony, V., Morse, K., Tibshirani, R., Hastie, T., Harrington, R., et al. (2021) · NEJM Catalyst

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 24

Random samples and probabilities

Efron, B., & Tibshirani, R. J. (1993) · An Introduction to the Bootstrap

Probability and Statistical ResearchSurvey Sampling and Estimation Techniques

Cited by 20

Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic

Sun, M. W., Troxell, D., & Tibshirani, R. (2023) · Scientific Reports

COVID-19 epidemiological studiesCOVID-19 Prevention and Impact

Cited by 7