作成者 |
|
|
|
|
|
本文言語 |
|
出版者 |
|
|
発行日 |
|
収録物名 |
|
巻 |
|
出版タイプ |
|
アクセス権 |
|
関連DOI |
|
|
関連URI |
|
|
関連情報 |
|
|
概要 |
We propose a method for algorithmic learning of transmembrane domains based 0n elementary formal systems. An elementary formal system (EFS, for short) is a kind of a logic program consisting of ifthen... rules. With this framework, we have implemented the algorithm for identifying transmembrane domains in amino acid sequences. Because of the limitations on computational resources, we 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, our 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 nontransmembrane sequences of length around 30 randomly chosen from PIR.続きを見る
|