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Anshul Kundaje

  • PhD
  • Assistant Professor
  • Stanford University School of Medicine
  • 63PublicationsNumber of items in Anshul's My Publications folder on Mendeley.
  • 36Followers

Recent publications

  • Opportunities and obstacles for deep learning in biology and medicine

    • Ching T
    • Himmelstein D
    • Beaulieu-Jones B
    • et al.
    Get full text
  • Challenges and recommendations for epigenomics in precision health

    • Carter A
    • Chang H
    • Church G
    • et al.

Professional experience

Assistant Professor

Stanford University, School of Medicine

September 2013 - Present

Research Scientist

Massachusetts Institute of Technology

March 2012 - August 2013(a year)

Postdoctoral Affiliate

Stanford University

October 2008 - February 2012(3 years)


My primary research interests are computational biology and applied machine learning with a focus on gene regulation. Our research focusses on development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for 1. Adaptive thresholding and normalization of massive collections of functional genomic data (e.g. ChIP-seq and DNase-seq) 2. Dissecting combinatorial transcription factor co-occupancy within and across cell-types 3. Predicting cell-type specific enhancers from chromatin state profiles 4. Exploiting expression and chromatin co-dynamics to predict enhancer-promoter interactions 5. Jointly modeling sequence grammars at regulatory elements and their chromatin state dynamics, expression changes of regulators and functional interaction data to learn unified multi-scale gene regulation programs 6. Elucidating the heterogeneity of chromatin architecture at regulatory elements 7. Improving the detection and interpretation of potentially causal disease-associated variants from genome-wide association studies More recently, we have also been developing methods to 1. Decipher the functional heterogeneity of transcription factor binding 2. Infer causal regulatory models by integrating diverse functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments 3. Model the complex relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals 4. Deep learning frameworks for functional genomics


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