Statistical algorithms and software for genomics

There are many large system problems that are hard to model exactly or in a computationally tractable fashion. Examples include the mapping of human DNA, speech recognition, and automated learning in computer chess. Traditional artificial intelligence solution techniques for such problems rely on a combination of custom encoding of expert knowledge and heuristic search. They take much time to hand craft and then often are unable to take advantage of faster computers as they become available. In this context, the authors explore the advantage of using statistical search techniques in which the knowledge is encoded in some form of statistical model whose parameters are automatically adjusted or trained with domain data. The benefits are faster development times, greater solution accuracy (compared to hand crafted solutions) and the ability to allow the problem size and desired solution accuracy to be scaled up with computational resources. They apply this approach to certain critical computational problems in mapping the human genome. They use a Bayesian model to provide the best solution accuracy as a function of the number of parameters. Heuristic search techniques derived from artificial intelligence are used to search the model space in an efficient manner in the average case