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Michael Beer, PhD

Michael Beer
Research Interests: Computational regulatory genomics
Lab Website: Beer Lab
Miller Research Building 467
Research Interests

Research Interests

The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence. Recently progress has been made in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches.

Beer’s work uses functional genomics DNase-seq, ChIP-seq, RNA-seq, and chromatin state data to computationally identify combinations of transcription factor binding sites which operate to define the activity of cell-type specific enhancers. Current focus is on:

  • improving SVM methodology by including more general sequence features and constraints
  • predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases
  • experimentally assessing the predicted impact of regulatory element mutation in mammalian cells
  • systematically determining regulatory element logic from ENCODE human and mouse data
  • using this sequence based regulatory code to assess common modes of regulatory element evolution and variation

Dr. Beer’s lab is located in the McKusick-Nathans Institute for Genetic Medicine, and the Department of Biomedical Engineering, which has long been a leader in the development of rigorous quantitative modeling of biological systems, and is a natural home for graduate studies in bioinformatics and computational biology at Johns Hopkins, including research in genomics, systems biology, machine learning, and network modeling.

Titles & Affiliations


  • Professor, Biomedical Engineering
  • Professor, Genetic Medicine

Affiliated Centers & Institutes



  • PhD, Princeton University, 1995
  • MA, Princeton University, 1991
  • BSE, University of Michigan, 1989
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