A learning algorithm for elementary formal systems and its experiments on identification of transmembrane domains

Proposes a method for algorithmic learning of transmembrane domains based on elementary formal systems. An elementary formal system (EFS) is a kind of a logic program consisting of if-then rules. With this framework, the authors have implemented the algorithm for identifying transmembrane domains in amino acid sequences. Because of the limitations on computational resources, they restrict candidate hypotheses to EFSs defined by collections of regular patterns. From 70 transmembrane sequences and a similar amount of negative examples which are not transmembrane sequences, the algorithm has produced several reasonable hypotheses of small size. Experiments with the database PIR show that one of them recognizes 95% of 689 transmembrane sequences and 95% of 19256 negative examples which consist of non-transmembrane sequences of length around 30 randomly chosen from PIR