MICHAEL N. MOLLA, Ph.D.

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Center for BioDynamics Fellow

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News: January 2008, Quoted in Drug Design and Development Magazine article on SNP calling. November 2007, Guest Author on the PLoS Biology blog: Roll Credits: Sometimes the Authorship Byline Isn't Enough

Room 104A Life Science and Engineering Building Boston University 24 Cummington St. Boston, MA 02215

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It is clear that high-throughput techniques, such as rapid DNA sequencing and gene chips, are changing the science of genetics. Hypothesis-driven science is now strongly complemented by these newer data-driven approaches. Over the course of the past decade, DNA microarrays, also known as gene chips, have come into prominence for genetic-level analysis throughout the life sciences. Using these microarrays, a scientist is able to perform hundreds of thousands of experiments on the surface of a single one-inch-by-one-inch wafer in the space of a single afternoon, generating more data than an army of researchers could have a generation ago. This potential flood of data brings many informatic challenges in both analysis and design. It is well understood that computer science will play a crucial role in their development and application. My research makes use of machine learning and other computational methods to perform central tasks in high-throughput biology. These tasks include gene-chip design, detection of genomic variation, and the interpretation of gene-expression patterns.

• T. Albert, M. Molla, D. Muzny, L. Nazareth, D. Wheeler, X. Song, T. Richmond, C. Middle, M. Rodesch, C. Packard, G. Weinstock, and R. Gibbs (2007)
  Direct Selection of Human Genomic Loci by Microarray Hybridization Nature Methods, 4:11 pp 907-909.


• E. Haag & M. Molla (2005).
  Compensatory evolution of interacting gene products through multifunctional intermediates. Evolution, 59, pp. 1620-32.
  
  
• M. Molla, J. Shavlik, T. Albert, T. Richmond & S. Smith(2004).
  A Self-Tuning Method for One-Chip SNP Identification. Proceedings of the IEEE Conference on Computer Systems Bioinformatics (CSB 2004), Stanford, CA
  
  
• M. Molla, M. Waddell, D. Page & J. Shavlik (2004).
  Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Magazine, 25, pp. 23-44.
  
  

Choose from the following links:

Publication List, C.V.

Applied Biodynamics Laboratory (Where Michael Molla Works)

Michael Molla's Academic Genealogy: (AIGP) (MGP)

2006 Summer Research Program Bioinformatics Lectures