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Biology and Biochemistry

D-Index
70
Citations
61691
World Ranking
6825
National Ranking
3147

Overview

Timothy L. Bailey is affiliated with the University of Nevada Reno in the United States. Their research primarily falls within the field of Biochemistry, Genetics, and Molecular Biology, with a specific focus on Molecular Biology. The research interests cover several key topics including Genomics and Chromatin Dynamics, RNA and protein synthesis mechanisms, Genomics and Phylogenetic Studies, RNA Research and Splicing, RNA modifications and cancer, and Epigenetics and DNA Methylation.

The scientist has published research work in several notable venues. These include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Genome biology
  • eLife

Some recent papers authored or co-authored by Timothy L. Bailey illustrate their contributions to the field. These works include:

  • STREME: accurate and versatile sequence motif discovery (2021, Bioinformatics)
  • SEA: Simple Enrichment Analysis of motifs (2021, bioRxiv, Cold Spring Harbor Laboratory)
  • XSTREME: Comprehensive motif analysis of biological sequence datasets (2021, bioRxiv, Cold Spring Harbor Laboratory)
  • STREME: Accurate and versatile sequence motif discovery (2020, bioRxiv, Cold Spring Harbor Laboratory)
  • T-Gene: improved target gene prediction (2020, Bioinformatics)

Frequent collaborators in the scientist's research include several individuals:

  • Charles E. Grant
  • Timothy O'Connor
  • Mikael Bodén
  • Coby Viner
  • Charles A. Ishak

The scientist's work engages deeply with genomic and molecular biology techniques aimed at understanding sequence motifs, enrichment analyses, and gene target prediction. These efforts contribute to the broader understanding of molecular mechanisms such as RNA synthesis, chromatin dynamics, and epigenetic modifications.

Best Publications

  • MEME Suite: tools for motif discovery and searching

    Timothy L. Bailey;Mikael Bodén;Fabian A. Buske;Martin C. Frith

  • Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

    Timothy L. Bailey;Charles Elkan

  • The MEME Suite

    Timothy L. Bailey;James Johnson;Charles E. Grant;William S. Noble

  • FIMO: scanning for occurrences of a given motif.

    Charles E. Grant;Timothy L. Bailey;William Stafford Noble

  • The Transcriptional Landscape of the Mammalian Genome

    P. Carninci;T. Kasukawa;S. Katayama;J. Gough

  • MEME: discovering and analyzing DNA and protein sequence motifs

    Timothy L. Bailey;Nadya Williams;Chris Misleh;Wilfred W. Li

  • Quantifying similarity between motifs

    Shobhit Gupta;John A Stamatoyannopoulos;Timothy L Bailey;William Stafford Noble

  • MEME-ChIP

    Philip Machanick;Timothy L. Bailey

  • Assessing computational tools for the discovery of transcription factor binding sites.

    Martin Tompa;Nan Li;Timothy L. Bailey;George M. Church

  • Combining evidence using p-values: application to sequence homology searches.

    Timothy L. Bailey;Michael Gribskov

  • DREME: motif discovery in transcription factor ChIP-seq data

    Timothy L. Bailey

  • Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization

    Timothy L. Bailey;Charles Elkan

  • The value of prior knowledge in discovering motifs with MEME.

    Timothy L. Bailey;Charles Elkan

  • The Performance and Usability of a Factory-Calibrated Flash Glucose Monitoring System.

    Timothy Bailey;Bruce W. Bode;Mark P. Christiansen;Leslie J. Klaff

  • Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial.

    Satish Garg;Howard Zisser;Sherwyn Schwartz;Timothy Bailey

  • Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data.

    Robert C McLeay;Timothy L Bailey

  • Defining the RGG/RG Motif

    Palaniraja Thandapani;Timothy R. O’Connor;Timothy L. Bailey;Stéphane Richard

  • Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts

    Ferhat Ay;Timothy L. Bailey;William Stafford Noble

  • The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

    Harukazu Suzuki;Alistair R.R. Forrest;Erik Van Nimwegen;Carsten O. Daub

  • Inferring direct DNA binding from ChIP-seq

    Timothy L. Bailey;Philip Machanick

Frequent Co-Authors

William Stafford Noble
William Stafford Noble University of Washington
Andrew C. Perkins
Andrew C. Perkins Monash University
Linda J. Richards
Linda J. Richards Washington University in St. Louis
Richard M. Gronostajski
Richard M. Gronostajski University at Buffalo, State University of New York
Charles Elkan
Charles Elkan University of California, San Diego
Martin C. Frith
Martin C. Frith University of Tokyo
Sean M. Grimmond
Sean M. Grimmond University of Melbourne
John S. Mattick
John S. Mattick University of New South Wales
Michael A. Nauck
Michael A. Nauck Ruhr University Bochum

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