Automated annotation of keywords for proteins related to mycoplasmataceae using machine learning techniques

Motivation: With the increase in submission of sequences to public databases, the curators of these are not able to cope with the amount of information. The motivation of this work is to generate a system for automated annotation of data we are particularly interested in, namely proteins related to the Mycoplasmataceae family. Following previous works on automatic annotation using symbolic machine learning techniques, the present work proposes a method of automatic annotation of keywords (a part of the SWISS-PROT annotation procedure), and the validation, by an expert, of the annotation rules generated. The aim of this procedure is twofold: to complete the annotation of keywords of those proteins which is far from adequate, and to produce a prototype of the validation environment, which is aimed at an expert who does not have a deep knowledge of the structure of the current databases containing the necessary information s/he needs. Results: As for the first objective, a rate of correct keywords annotation of 60% is reported in the literature. Our preliminary results show that with a slightly different method, applied this method to data related to Mycoplasmataceae only, we are able to increase that rate of correct annotation.