Butte, Atul J.

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology

Interstitial Lung Diseases and Idiopathic Pulmonary FibrosisSingle-cell and spatial transcriptomics

Cited by 4,984

xCell: digitally portraying the tissue cellular heterogeneity landscape

Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 4,397

The repertoire of mutational signatures in human cancer

Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Tian Ng, A. W., Wu, Y., Boot, A., Covington, K. R., Gordenin, D. A., Bergstrom, E. N., et al. (2020) · Nature

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 3,496

Pan-cancer analysis of whole genomes

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen, L. A., Abascal, F., Abeshouse, A., Aburatani, H., Adams, D. J., Agrawal, N., Ahn, K. S., Ahn, S.-M., Aikata, H., et al. (2020) · Nature

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 3,064

Scalable and accurate deep learning with electronic health records

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 2,648

Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: Potential role of PGC1 and NRF1

Patti, M. E., Butte, A. J., Crunkhorn, S., Cusi, K., Berria, R., Kashyap, S., Miyazaki, Y., Kohane, I., Costello, M., Saccone, R., et al. (2003) · Proceedings of the National Academy of Sciences

Metabolism, Diabetes, and CancerAdipose Tissue and Metabolism

Cited by 1,952

Personal omics profiling reveals dynamic molecular and medical phenotypes

Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell

Bioinformatics and Genomic NetworksHealth, Environment, Cognitive Aging

Cited by 1,291

Variation in the Human Immune System Is Largely Driven by Non-Heritable Influences

Brodin, P., Jojic, V., Gao, T., Bhattacharya, S., Angel, C. J. L., Furman, D., Shen-Orr, S., Dekker, C. L., Swan, G. E., Butte, A. J., et al. (2015) · Cell

Genetic Associations and EpidemiologyImmune responses and vaccinations

Cited by 1,065

The evolutionary history of 2,658 cancers

Gerstung, M., Jolly, C., Leshchiner, I., Dentro, S. C., Gonzalez, S., Rosebrock, D., Mitchell, T. J., Rubanova, Y., Anur, P., Yu, K., et al. (2020) · Nature

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 1,057

Patterns of somatic structural variation in human cancer genomes

Li, Y., Roberts, N. D., Wala, J. A., Shapira, O., Schumacher, S. E., Kumar, K., Khurana, E., Waszak, S., Korbel, J. O., Haber, J. E., et al. (2020) · Nature

Cancer Genomics and DiagnosticsGenomic variations and chromosomal abnormalities

Cited by 867

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Cortés-Ciriano, I., Lee, J. J.-K., Xi, R., Jain, D., Jung, Y. L., Yang, L., Gordenin, D., Klimczak, L. J., Zhang, C.-Z., Pellman, D. S., et al. (2020) · Nature Genetics

Cancer Genomics and DiagnosticsGenomic variations and chromosomal abnormalities

Cited by 712

Clinical assessment incorporating a personal genome

Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., Dudley, J. T., Ormond, K. E., Pavlovic, A., Morgan, A. A., et al. (2010) · The Lancet

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 674

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.

Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 665

Ethnic Differences in the Relationship Between Insulin Sensitivity and Insulin Response

Kodama, K., Tojjar, D., Yamada, S., Toda, K., Patel, C. J., & Butte, A. J. (2013) · Diabetes Care

Diabetes, Cardiovascular Risks, and LipoproteinsPancreatic function and diabetes

Cited by 599

FoxO3 Regulates Neural Stem Cell Homeostasis

Renault, V. M., Rafalski, V. A., Morgan, A. A., Salih, D. A. M., Brett, J. O., Webb, A. E., Villeda, S. A., Thekkat, P. U., Guillerey, C., Denko, N. C., et al. (2009) · Cell Stem Cell

Neurogenesis and neuroplasticity mechanismsFOXO transcription factor regulation

Cited by 586

Severe hypothyroidism caused by type 3 iodothyronine deiodinase in infantile hemangiomas

Huang, S. A., Tu, H. M., Harney, J. W., Venihaki, M., Butte, A. J., Kozakewich, H. P. W., Fishman, S. J., & Larsen, P. R. (2000) · New England Journal of Medicine

Thyroid Disorders and TreatmentsVascular Malformations and Hemangiomas

Cited by 540

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Zack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., Jurafsky, D., Szolovits, P., Bates, D. W., Abdulnour, R.-E. E., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 510

A Longitudinal Big Data Approach for Precision Health

Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., et al. (2019) · Nature Medicine

Health, Environment, Cognitive AgingMetabolomics and Mass Spectrometry Studies

Cited by 494

The Promise of Digital Health: Then, Now, and the Future

Verily, Abernethy, A., Adams, L., National Academy of Medicine, Barrett, M., ResMed, Bechtel, C., Health, X., Brennan, P., Medicine, N. L. O., et al. (2022) · NAM Perspectives

Mobile Health and mHealth ApplicationsTechnology and Human Factors in Education and Health

Cited by 477

Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

Rodriguez-Martin, B., Alvarez, E. G., Baez-Ortega, A., Zamora, J., Supek, F., Demeulemeester, J., Santamarina, M., Ju, Y. S., Temes, J., Garcia-Souto, D., et al. (2020) · Nature Genetics

Genomic variations and chromosomal abnormalitiesCancer Genomics and Diagnostics

Cited by 451

Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

Wolfe, C. J., Kohane, I. S., & Butte, A. J. (2005) · BMC Bioinformatics

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 422

Ethical and regulatory challenges of large language models in medicine

Ong, J. C. L., Chang, S. Y.-H., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi-Velez, F., Lu, W., Savulescu, J., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 342

Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer

Akdemir, K. C., Le, V. T., Chandran, S., Li, Y., Verhaak, R. G., Beroukhim, R., Campbell, P. J., Chin, L., Dixon, J. R., Futreal, P. A., et al. (2020) · Nature Genetics

Genomics and Chromatin DynamicsChromatin Remodeling and Cancer

Cited by 283

Prediction of preadipocyte differentiation by gene expression reveals role of insulin receptor substrates and necdin

Tseng, Y.-H., Butte, A. J., Kokkotou, E., Yechoor, V. K., Taniguchi, C. M., Kriauciunas, K. M., Cypess, A. M., Niinobe, M., Yoshikawa, K., Patti, M. E., et al. (2005) · Nature Cell Biology

Adipose Tissue and MetabolismPeroxisome Proliferator-Activated Receptors

Cited by 222