(1) Mortal Polygamous Networks (2) Probabilistic models for computational gene prediction and gene expression analysis
by (1) James Woodcock (2) Ben Lansdell
Abstract: (1) Mortal Polygamous Networks, by James Woodcock
This talk focuses upon the use of random walks to model the growth of stochastically evolving networks, whose nodes undergo birth, death and marriage events at random intervals.
(2) Probabilistic models for computational gene prediction and gene expression analysis, by Ben Lansdell
The genes that describe an organism typically comprise a small percentage of its genome. Determining their location and structure in a sequenced genome is a non-trivial task which lends itself well to the computational and statistical methods of bioinformatics. In this talk I will outline the problem of gene prediction and describe a common hidden Markov model used for its solution. An attempt is made to improve current methods by modeling gene expression data in addition to DNA sequence data.
For More Information: Contact: Mat Simpson M.Simpson@ms.unimelb.edu.au