Algebraic Statistics for Computational Biology,
edited by Lior Pachter and Bernd Sturmfels, published by Cambridge University Press (Fall., 2005).
Ruriko Yoshida, Assistant Research Professor of Mathematics
Office: 222 Physics Building Phone: (919) 660-2827 Fax: (919) 660-2821 E-mail: ruriko@math.duke.edu Office Hours: TBA
MWF 11:55 AM - 12:45 PM, ROOM 228E Physics
We will focus on interactions between the theory of algebraic statistics and the applications of computational biology. Recent work shows that we can apply theory in algebraic statistics to developing algorithms for problems in biology and also problems in computational biology enhance research in algebraic statistics. We will cover mainly Part I, Chapter 1, 2, 3, and 4. If time permits, we will discuss Part II.
www.math.duke.edu/~ruriko/courses/math295/Schedule.html
Background in discrete applied mathematics. Experience in algebra and/or combinatorics preferred, but not necessary. Familiar with basic biology will be helpful but this is not requirement.
Research project and homework problems.
Return to: Duke University * Mathematics DepartmentR. Durbin, S. Eddy, A. Krogh, G. Mitchison, Biological Sequence Analysis, Cambridge University Press (1998)- Application of graphical models to problems in biological sequence analysis.
G. Pistone, E. Riccomagno, H.P. Wynn, Algebraic Statistics: Computational Commutative Algebra in Statistics, CRC Press (2000)- Contingency tables and other applications of algebraic statistics
D. Cox, D. O'Shea, J.B. Little, Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, Springer Verlag (1996)- Excellent introduction to concrete algebra.
C. Semple, M. Steel, Phylogenetics, Oxford University Press (2004) Mathematical foundations of phylogenetics.