Gene ontology classification: Building high-level knowledge using genetic algorithms

Computational approaches have been applied in many different biology application domains. When such tools are based on conventional computation, they have shown limitations to approach complex biological problems. In the present study, a computational evolutionary environment (CEE) is proposed as tool to extract classification rules from biological datasets. The main goal of the proposed approach is to allow the discovery of concise, yet accurate, high-level rules (from a biological database) which can be used as a classification system. More than focusing only on the classification accuracy, the proposed CEE model aims at balancing prediction precision, interpretability and comprehensibility. The obtained results show that the proposed CEE is promising and capable of extracting useful high-level knowledge that could not be extracted by traditional classifications methods such as Decision Trees, One R and the Single Conjunctive Rule Learner using the same dataset.