GEARS: A Genetic Algorithm Based Machine Learning Technique to Develop Prediction Models

The development of new prediction models to identify potential modified residues are based on different machine learning methods. Primary sequences, biChemical properties of the amino acids and 3D structural information of proteins are used to evolve prediction models. The information about the significant residues to govern different biological prCesses has not been considered yet to develop a prediction model. MAPRes is an efficient tool which has been utilized to mine significant residues and assCiation patterns for surrounding amino acids of some specific modifications on hydroxyl and amino group such as phosphorylation and acetylation. The primary sequences of the proteins and assCiation patterns of surrounding amino acids of modified residues may use to train new dataset for the development of an efficient and reliable prediction model. Biophysical and biChemical properties of the amino acids are also important parameters for the prediction of the modified residues. This study proposes, GEARS (Genetic Evolution of ClAssifers by Learning Residue Rules and Sequences), a classifier rule learning model, which considered different machine learning techniques such as ANNs, HMM and MAPRes were considered for the development of GEARS model. The GEARS, by combining these models, will have the capacity to reduce the false negative and positive predictions.