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Michael A. Beer, M.A., Ph.D.

Headshot of Michael A. Beer
  • Professor of Biomedical Engineering

Research Interests

Computational regulatory genomics and machine learning more


Dr. Michael Beer is an associate professor of biomedical engineering at the Johns Hopkins University School of Medicine. His research focuses on understanding how gene regulatory information is encoded in genomic DNA sequence and how regulatory variation contributes to diseases. His lab recently develops machine-learning techniques where computers algorithms detect regulatory sequences in intergenic DNA.

Dr. Beer received his undergraduate degree from the University of Michigan. He earned his Ph.D. in astrophysical sciences from Princeton University. Dr. Beer joined the Johns Hopkins faculty in 2005.

Prior to joining Johns Hopkins, Dr. Beer was the Lewis Thomas Postdoctoral Fellow in the Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics at Princeton University.

Dr. Beer was recognized with the Simon Ramo Award for his theses in plasma physics. He also was awarded the DOE Fusion Energy Postdoctoral Fellowship and the National Science Foundation Graduate Fellowship, and the Searle Scholars Award for promising junior facility. more


  • Professor of Biomedical Engineering
  • Joint Appointment in Molecular Biology and Genetics
  • Professor of Genetic Medicine

Departments / Divisions

Centers & Institutes



  • M.A.; Princeton University (New Jersey) (1991)
  • Ph.D.; Princeton University (New Jersey) (1995)
  • B.S.E.; University of Michigan (Michigan) (1989)

Additional Training

  • Princeton University, Princeton, NJ, 2005, Integrative Genomics

Research & Publications

Research Summary

Dr. Beer’s research focuses on understanding how gene regulatory information is encoded in genomic DNA sequence. 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.

His current research 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; and using this sequence based regulatory code to assess common modes of regulatory element evolution and variation.


Lab Website: Beer Lab

Selected Publications

View all on PubMed

Lee D, Gorkin DU, Baker  M, Strober BJ, Asoni AL, Beer, MA, McCallion AS. "A Method to Predict the Impact of Regulatory Variants from DNA Sequence." Nature Genetics 47 (8), 955-961. 2015.

Beer MA. "Predicting Enhancer Activity and Variant Impact using gkm-SVM." Human Mutation 8 (9), 1251-1258 (2017).

Ganhdi M, Lee D, Mohammad-Noori, M, Beer MA. "Enhanced Regulatory Sequence Predicition Using Grapper K-Mer Features"  PLoS Comput Biol 10 (7), e1003711

Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, Sandstorm R, Z Ma, et al.  "A Comparative Encyclopedia of DNA Elements in the Mouse Genome."" Nature 515 (7527), 355-364. 2014.

Ghandi M, Mohammad-Noori M, Ghareghani N, Lee D, Garraway L, and Beer MA. "gkmSVM, an R package for gapped-kmer SVM." Bioinformatics 32 (14), 2205-2207 (2016).

Academic Affiliations & Courses

Graduate Program Affiliation

Preceptor-Predoctoral Training Program in Human Genetics

Activities & Honors


  • Lewis Thomas Postdoctoral Fellowship
  • Fusion Energy Postdoctoral Fellowship, DOE
  • Graduate Fellowship, National Science Foundation
  • Simon Ramo Award (now named the Marshall N. Rosenbluth Outstanding Doctoral Thesis in Plasma Physics Award), 1996
  • Searle Scholar Award
  • Johns Hopkins Alumni Association Teaching Award
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