World's Best Scientists 2026 revealed!

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Computer Science

D-Index
103
Citations
48065
World Ranking
320
National Ranking
176

Genetics

D-Index
117
Citations
60762
World Ranking
421
National Ranking
218

Medicine

D-Index
117
Citations
61697
World Ranking
4155
National Ranking
2279

Research.com Recognitions

  • 2014 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2009 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

Russ B. Altman is affiliated with Stanford University in the United States. Their research spans a range of topics primarily in the fields of biochemistry, genetics, molecular biology, and medicine.

Altman's work encompasses several specialized subfields, including molecular biology, genetics, computational theory and mathematics, pharmacology, and artificial intelligence. This multidisciplinary focus supports investigations into computational drug discovery methods, bioinformatics and genomic networks, machine learning applications in bioinformatics, RNA and protein synthesis mechanisms, protein structure and dynamics, pharmacogenetics and drug metabolism, as well as biomedical text mining and ontologies.

Frequent publication venues for Altman include bioRxiv (Cold Spring Harbor Laboratory), Zenodo (CERN European Organization for Nuclear Research), Biophysical Journal, arXiv (Cornell University), and Clinical Pharmacology & Therapeutics. These platforms feature a significant number of Altman's contributions.

Significant recent papers authored or co-authored by Altman include:

  • On the Opportunities and Risks of Foundation Models (2021), published in arXiv (Cornell University)
  • Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms (2020), published in JAMA
  • Protein Sequence Design with a Learned Potential (2022), published in Nature Communications
  • Pharmacogenetics at Scale: An Analysis of the UK Biobank (2020), published in Clinical Pharmacology & Therapeutics
  • MARS: Discovering Novel Cell Types Across Heterogeneous Single-Cell Experiments (2020), published in Nature Methods

Altman has collaborated frequently with a number of co-authors over their career. Notably, these collaborators include Alexander Derry, Scott C. Blanchard, Raphael J.L. Townshend, Martin Vögele, and Patricia Suriana.

Altman has been recognized by professional societies through fellowships, including being named a Fellow of the American Association for the Advancement of Science (AAAS) in 2014 and a Fellow of the Indian National Academy of Engineering (INAE) in 2009.

Best Publications

  • Missing value estimation methods for DNA microarrays.

    Olga G. Troyanskaya;Michael N. Cantor;Gavin Sherlock;Patrick O. Brown

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Pharmacogenomics Knowledge for Personalized Medicine

    M Whirl-Carrillo;E M McDonagh;J M Hebert;L Gong

  • Doxorubicin pathways: pharmacodynamics and adverse effects

    Caroline F. Thorn;Connie Oshiro;Sharon Marsh;Tina Hernandez-Boussard

  • Estimation of the warfarin dose with clinical and pharmacogenetic data

    Klein Te;Altman Rb;Eriksson N

  • Diversity of gene expression in adenocarcinoma of the lung

    Mitchell E. Garber;Olga G. Troyanskaya;Karsten Schluens;Simone Petersen

  • Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

    Rui Chen;George I. Mias;Jennifer Li-Pook-Than;Lihua Jiang

  • Guidelines for investigating causality of sequence variants in human disease

    D G MacArthur;T A Manolio;D P Dimmock;H L Rehm

  • The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes

    Maureen E. Hillenmeyer;Eula Fung;Jan Wildenhain;Sarah E. Pierce

  • PRINCIPAL COMPONENTS ANALYSIS TO SUMMARIZE MICROARRAY EXPERIMENTS: APPLICATION TO SPORULATION TIME SERIES

    Soumya Raychaudhuri;Joshua M. Stuart;Russ B. Altman

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman

  • Machine learning in chemoinformatics and drug discovery.

    Yu Chen Lo;Stefano E. Rensi;Wen Torng;Russ B. Altman

  • Data-Driven Prediction of Drug Effects and Interactions

    Nicholas P. Tatonetti;Patrick P. Ye;Roxana Daneshjou;Russ B. Altman

  • Clinical assessment incorporating a personal genome

    Euan A Ashley;Atul J Butte;Matthew T Wheeler;Rong Chen

  • Human induced pluripotent stem cell–derived cardiomyocytes recapitulate the predilection of breast cancer patients to doxorubicin-induced cardiotoxicity

    Paul W. Burridge;Yong Fuga Li;Elena Matsa;Haodi Wu

  • A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)

    Olga G. Troyanskaya;Kara Dolinski;Art B. Owen;Russ B. Altman

  • Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing

    J. A. Johnson;L. Gong;M. Whirl-Carrillo;B. F. Gage

  • Method for monitoring and/or modifying web browsing sessions

    Ramon M. Felciano;Russ B. Altman

  • Metformin pathways: pharmacokinetics and pharmacodynamics

    Li Gong;Srijib Goswami;Kathleen M. Giacomini;Russ B. Altman

  • Clinical Interpretation and Implications of Whole-Genome Sequencing

    Frederick E. Dewey;Megan E. Grove;Cuiping Pan;Benjamin A. Goldstein

Frequent Co-Authors

Teri E. Klein
Teri E. Klein Stanford University
Lawrence Hunter
Lawrence Hunter University of Colorado Denver
Julie A. Johnson
Julie A. Johnson University of Florida
Daniel L. Rubin
Daniel L. Rubin Stanford University
Jeffrey T. Chang
Jeffrey T. Chang The University of Texas Health Science Center at Houston
Michael Snyder
Michael Snyder Stanford University
Oleg Jardetzky
Oleg Jardetzky Stanford University
Dan M. Roden
Dan M. Roden Vanderbilt University Medical Center
Isaac S. Kohane
Isaac S. Kohane Harvard University
Soumya Raychaudhuri
Soumya Raychaudhuri Brigham and Women's Hospital

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