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Using Machine Learning Algorithms For E . coli SOS Boxes Detection

The present work evaluates how non-conventional methods of computation, like Neural Networks [3] and Bayesian Networks [6], can be used to recognize patterns in genomes [5]. The pattern we will be dealing with is called SOS box, and these boxes are part of a regulatory system responsible for damage repair in the DNA [6]. About 30 SOS boxes [1][2] are known in E. coli K-12, which is the organism we chose to work with in the present analysis. The consensus sequence of the SOS box (TA CTG TATATATATA CAG TA) is one of the characteristics most used to detect SOS boxes in the E. coli genome. If a search based on that characteristic takes place in E. coli genome, many of the sequences found are not part of the SOS regulatory system. One of the goals of the present work is the attempt to distinguish between the sequences that actually are part of the SOS system from the ones that are not but seem to be SOS boxes. Both Neural and Bayesian Networks are able to learn patterns directly by contact with positive and negative examples. Tests done so far have allowed us to acquire many heuristics about this problem, regarding mainly: The architecture and parameters to be applied in Neural Networks; The structures for the Bayesian Networks; How to pre-process and handle the examples to increase the learning capacity of the networks; How to post-process and analyse the results observed. From these tests, we have selected the five Neural Networks with highest performance (accuracy and sensibility), and tested them in sequences located between 1pb to 150pb upstream of all E. coli genes. The results were crossed with each other, looking for sequences that all the Neural Networks scored high. From the analysis of this outcome, two sequences showed up as good suggestions of new SOS boxes. Both sequences are located less that 100pb away from the start codon of hypothetical genes. One of these two suggestions was classified as a positive instance (above 50%) in all of the six Bayesian Networks chosen, and the other, in four of them. The heuristics obtained will be applied in future studies to refine the suggestion of potential SOS boxes, to compare these methods with others used in previous works [1][2], and to generalize the heuristics obtained to phylogenetic related organisms.