Learning local languages and its application to protein /spl alpha/-chain identification

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