Genetic Complementary Learning for Translation Initialization Sites Prediction

Accurate prediction of the translation initiation sites (TIS) in eukaryotes is paramount for better understanding of the translation process, gene structure, as well as protein coding, and for more reliable amino acid prediction, etc. However, detecting TIS is not a simple task. Hence, computational biology is adopted to assist in the detection. Unfortunately, some computational biology tools do not provide means for facilitating the knowledge extraction or system validation. Also, they have neither biological interpretation nor human-like reasoning process. Realizing that, a novel Genetic Complementary Learning (GCL) fuzzy neural network, which based on gene selection process, is proposed. GCL inherits some advantageous traits from three worlds: the dynamics from genetic algorithm, the good pattern recognition performance from complementary learning, as well as the interpretable, autonomous, and human-like operations from fuzzy neural network From experimental result, GCL demonstrates itself as a competent tool for TIS prediction.