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Michael A. Langston PhD: Texas A & M University 203 Claxton Complex |
Keywords:
Computational genomics, discrete optimization, graph algorithms, high-performance platforms, genome-scale implementations, microarray analysis, pathway homology, regulatory network analysis.
Research Area:
Analysis of high-throughput proteomic, transcriptomic and other sorts of -omic data.
Description of Research:
Professor Langston's research is focused novel combinatorial optimization methods and their application in the analysis of high-throughput biological data. He and his team of students labor to unravel regulatory messages hidden in proteomic, transcriptomic and other sorts of -omic data. In the context of microarray data, for example, graph algorithms, genome-scale implementations and high-performance platforms are used in the genetic analysis of gene transcription. With them it is possible to address foundational questions such as: “are the sets of genes co-expressed under one type of conditions the same as those sets co-expressed under another?” Innovative algorithms for the extraction of dense subgraphs derived from correlation matrices help identify which, among a set of candidate genes, are the most likely regulators of trait variation. We seek to discover large groups of genetically co-expressed genes, and to extrapolate the consequences of this genetic variation on phenotypes observed across many levels of biological scale. This approach is furthermore applied to definitions of homology-based gene sets, and the incorporation of categorical data such as known gene pathways. In all these tasks discrete mathematics and combinatorial algorithms form organizing principles upon which methods and implementations are based.
Selected Publications:
- A. Fadiel, M. A. Langston, F. Naftolin, X. Peng, A. D. Perkins, P. Pevsner, H. S. Taylor, O. Tuncalp and D. Vitello. (2006). Computational Analysis of Mass Spectrometry Data Using Novel Combinatorial Methods. Proceedings, International Conference on Computer Systems and Applications.
- M. A. Langston, A. D. Perkins, A. M. Saxton, J. A. Scharff and B. H. Voy. (2006). Innovative Computational Methods for Transcriptomic Data Analysis. Proceedings, ACM Symposium on Applied Computing.
- E. J. Chesler, L. Lu, S. Shou, Y. Qu, J. Gu, J. Wang, H. C. Hsu, J. D. Mountz, N. E. Baldwin, M. A. Langston, D. W. Threadgill, K. F. Manly and R. W. Williams (2005). Complex Trait Analysis of Gene Expression Uncovers Polygenic and Pleiotropic Networks that Modulate Nervous System Function. Nature Genetics. 37: 233-242.
- N. E. Baldwin, E. J. Chesler, S. Kirov, M. A. Langston, J. R. Snoddy, R. W. Williams and Bing Zhang (2005). Computational, Integrative and Comparative Methods. Journal of Biomedicine and Biotechnology. 2: 172-180.
- Y. Zhang, F. N. Abu-Khzam, N. E. Baldwin, E. J. Chesler, M. A. Langston and N. F. Samatova (2005). Genome-Scale Computational Approaches to Memory-Intensive Applications in Systems Biology. Proceedings, Supercomputing.