Learning local languages and their application to DNA sequence analysis

This paper concerns an efficient algorithm for learning in the limit a special type of regular languages called strictly locally testable languages from positive data, and its application to identifying the protein a-chain region in amino acid sequences. First, we present a linear time algorithm that, given a strictly locally testable language, learns (identifies) its deterministic finite state automaton in the limit from only positive data. This provides us with a practical and efficient method for learning a specific concept domain of sequence analysis. We then describe several experimental results using the learning algorithm developed above. Following a theoretical observation which strongly suggests that a certain type of amino acid sequences can be expressed by a locally testable language, we apply the learning algorithm to identifying the protein a-chain region in amino acid sequences for hemoglobin. Experimental scores show an overall success rate of 95 percent correct identification for positive data, and 96 percent for negative data.