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Michael A. Beer, M.A., Ph.D.
Associate Professor of Biomedical Engineering
Research Interests: Bayesian networks and machine learning; Computational molecular biology and genomics; Combinatorial gene regulation
Dr. Michael Beer is an assistant 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. His research lab recently developed “machine-learning” techniques where computers were taught to identify regulating gene sequences in DNA.
Dr. Beer received his undergraduate degree from the University of Michigan. He earned his masters degree and 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.
- Associate Professor of Biomedical Engineering
- Joint Appointment in Molecular Biology and Genetics
- B.S.E., University of Michigan (Michigan) (1989)
- M.A., Princeton University (New Jersey) (1991)
- Ph.D., Princeton University (New Jersey) (1995)
Research & Publications
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.
Selected PublicationsView all on Pubmed
Lee D, Beer MA. "Mammalian Enhancer Prediction." Genome Analysis: Current Procedures and Applications. Horizon Press. 2014.
Ghandi M, Mohammad-Noori M, and Beer MA. "Robust k-mer Frequency Estimation Using Gapped k-mers." Journal of Mathematical Biology. 2013. (Epub ahead of print).
Fletez-Brant C*, Lee D*, McCallion AS and Beer MA. "kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic datasets." Nucleic Acids Research 41: W544–W556. 2013.
Gorkin DU, Lee D, Reed X, Fletez-Brant C, Blessling SL, Loftus SK, Beer MA, Pavan WJ, and McCallion AS. "Integration of ChIP-seq and Machine Learning Reveals Enhancers and a Predictive Regulatory Sequence Vocabulary in Melanocytes." Genome Research 22:2290–2301. 2012.
Lee D, Karchin R, and Beer MA. "Discriminative prediction of mammalian enhancers from DNA sequence." Genome Research 21:2167–2180. 2011.
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