A machine discovery from amino acid sequences by decision trees over regular patterns

This paper describes a machine learning system that discovered a “negative motif”, in transmembrane domain identification from amino acid sequences, and reports its experiments on protein data using PIR database. We introduce a decision tree whose nodes are labeled with regular patterns. As a hypothesis, the system produces such a decision tree for a small number of randomly chosen positive and negative examples from PIR. Experiments show that our system finds reasonable hypotheses very successfully. As a theoretical foundation, we show that the class of languages defined by decesion trees of depth at mostd overk-variable regular patterns is polynomial-time learnable in the sense of probably approximately correct (PAC) learning for any fixedd, k≥0.