Biosequence Analysis in PRISM

摘要

In this work, we consider probabilistic models that can infer biological information solely from biological sequences such as DNA. Traditionally, computational models for biological sequence analysis have been implemented in a wide variety of procedural and object oriented programming languages [1]. Models implemented using stochastic logic programming (SLP [2,3,4]) instead, may draw upon the benefits of increased expressive power, conciseness and compositional-ity. It does, however, pose a big challenge to design efficient SLP models.rnWe are currently experimenting with the optimization of a simple model for gene finders written in PRISM [4]. This model plays the role of a canonical model, supposed to hold the best knowledge available about genes, non genes and their respective distributions in DNA. We assume that the canonical model is not computationally practical per se.