Class Imbalance Methods for Translation Initiation Site Recognition

Abstract

Translation initiation sites (TIS) recognition is one of the first steps in gene structure prediction, and one of the common components in any gene recognition system. Many methods have been described in the literature to identify TIS in transcripts such as mRNA, EST and cDNA sequences. However, the recognition of TIS in DNA sequences is a far more challenging task, and the methods described so far for transcripts achieve poor results in DNA sequences. Most methods approach this problem taking into account its biological features. In this work we try a different view, considering this classification problem from a purely machine learning perspective.From the point of view of machine learning, TIS recognition is a class imbalance problem. Thus, in this paper we approach TIS recognition from this angle, and apply the different methods that have been developed to deal with imbalance datasets.Results show an advantage of class imbalance methods with respect to the same methods applied without considering the class imbalance nature of the problem. The applied methods are also able to improve the results obtained with the best method in the literature, which is based on looking for the next in-frame stop codon from the putative TIS that must be predicted.