RECOGNITION OF SPLICE JUNCTIONS ON DNA-SEQUENCES BY BRAIN LEARNING ALGORITHM

Motivation: The problem addressed in this paper is the prediction of s plice site locations in harman DNA. The aims of the proposed approach are explicit splicing rule description,, high recognition quality, and robust and stable 'one shot' data processing. Results: These results are achieved by means of a new learning algorithm [BRAIN (Batch Releva nce-based Artificial INtelligence)], described in the paper; inferring Boolean formulae from examples, and by considering the splicing rules as disjunctive normal form (DNF) formulae. The formula terms are comp uted in an iterative way, by identifying from the training set a relev ance coefficient for each attribute. The classification is then refine d by a neural network and combined with a discriminant analysis proced ure. This splice site recognition method shows low error rates (0.0002 and 0.0003) and high correlation coefficient measures (0.83 and 0.81) for donor and acceptor sites, respectively; better than other methods .