World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
30510
World Ranking
5737
National Ranking
2607

Overview

Jeffrey T. Leek is affiliated with the Fred Hutchinson Cancer Research Center in the United States. Their research spans multiple fields, including Biochemistry, Genetics and Molecular Biology, as well as Computer Science. Key subfields of study include Molecular Biology, Artificial Intelligence, Cancer Research, Information Systems and Management, and Computer Science Applications.

The scientist's work covers several main topics, particularly within Scientific Computing and Data Management, Cancer-related molecular mechanisms research, Genetics, Bioinformatics, and Biomedical Research. Other principal areas include Gene expression and cancer classification, RNA Research and Splicing, RNA modifications and cancer, and Machine Learning in Healthcare.

Frequent co-authors of Jeffrey T. Leek are:

  • Frederick J. Tan
  • Ava M. Hoffman
  • Candace Savonen
  • Tyler H. McCormick
  • Carrie Wright

Publications by Leek have appeared in venues such as arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Genome Research, F1000Research, and Nature. These venues reflect a focus on genomics, molecular biology, computational biology, and interdisciplinary applications involving computer science.

Selected recent papers include:

  • "Transparency and reproducibility in artificial intelligence," 2020, Nature
  • "recount3: summaries and queries for large-scale RNA-seq expression and splicing," 2021, Genome biology
  • "Functional annotation of human long noncoding RNAs via molecular phenotyping," 2020, Genome Research
  • "Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space," 2022, Cell Genomics
  • "Addressing 6 challenges in generative AI for digital health: A scoping review," 2024, PLOS Digital Health

Best Publications

  • Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

    Mihaela Pertea;Daehwan Kim;Geo M Pertea;Jeffrey T Leek

  • The sva package for removing batch effects and other unwanted variation in high-throughput experiments

    Jeffrey T. Leek;W. Evan Johnson;Hilary S. Parker;Andrew E. Jaffe

  • Tackling the widespread and critical impact of batch effects in high-throughput data

    Jeffrey T. Leek;Robert B. Scharpf;Héctor Corrada Bravo;Héctor Corrada Bravo;David Simcha

  • Capturing heterogeneity in gene expression studies by surrogate variable analysis.

    Jeffrey T Leek;John D Storey

  • Ballgown bridges the gap between transcriptome assembly and expression analysis.

    Alyssa C Frazee;Geo Pertea;Andrew E Jaffe;Ben Langmead

  • Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

    Andrew E Jaffe;Peter Murakami;Hwajin Lee;Jeffrey T Leek

  • Temporal dynamics and genetic control of transcription in the human prefrontal cortex

    Carlo Colantuoni;Barbara K. Lipska;Tianzhang Ye;Thomas M. Hyde;Thomas M. Hyde

  • Significance analysis of time course microarray experiments

    John D. Storey;Wenzhong Xiao;Jeffrey T. Leek;Ronald G. Tompkins

  • svaseq: removing batch effects and other unwanted noise from sequencing data

    Jeffrey T. Leek

  • Reproducible RNA-seq analysis using recount2.

    Leonardo Collado-Torres;Abhinav Nellore;Kai Kammers;Kai Kammers;Shannon E Ellis

  • The importance of transparency and reproducibility in artificial intelligence research

    Benjamin Haibe-Kains;George Alexandru Adam;Ahmed Hosny;Farnoosh Khodakarami

  • A general framework for multiple testing dependence

    Jeffrey T. Leek;John D. Storey

  • Cloud-scale RNA-sequencing differential expression analysis with Myrna

    Ben Langmead;Kasper D Hansen;Jeffrey T Leek

  • Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.

    Andrew E Jaffe;Richard E Straub;Joo Heon Shin;Ran Tao

  • On the design and analysis of gene expression studies in human populations.

    Joshua M Akey;Shameek Biswas;Jeffrey T Leek;John D Storey

  • Polyester: simulating RNA-seq datasets with differential transcript expression

    Alyssa C. Frazee;Andrew E. Jaffe;Ben Langmead;Jeffrey T. Leek

  • EDGE: extraction and analysis of differential gene expression

    Jeffrey T. Leek;Eva Monsen;Alan R. Dabney;John D. Storey

  • recount3: summaries and queries for large-scale RNA-seq expression and splicing

    Christopher Wilks;Shijie C Zheng;Feng Yong Chen;Rone Charles

  • Surrogate variable analysis

    Jeffrey Tullis Leek

  • Statistics: P values are just the tip of the iceberg

    Jeffrey T. Leek;Roger D. Peng

  • Sequencing technology does not eliminate biological variability

    Kasper D Hansen;Zhijin Wu;Rafael A Irizarry;Jeffrey T Leek

  • Opinion: Reproducible research can still be wrong: Adopting a prevention approach

    Jeffrey T. Leek;Roger D. Peng

  • ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

    Alyssa C Frazee;Ben Langmead;Jeffrey T Leek

  • Transparency and reproducibility in artificial intelligence.

    Benjamin Haibe-Kains;George Alexandru Adam;Ahmed Hosny;Farnoosh Khodakarami;Farnoosh Khodakarami

Frequent Co-Authors

Andrew E. Jaffe
Andrew E. Jaffe Johns Hopkins University
Ben Langmead
Ben Langmead Johns Hopkins University
Kasper D. Hansen
Kasper D. Hansen Johns Hopkins University
Rafael A. Irizarry
Rafael A. Irizarry Harvard University
Joel E. Kleinman
Joel E. Kleinman Johns Hopkins University
Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre
Lisa R. Yanek
Lisa R. Yanek Johns Hopkins University School of Medicine

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