Sequence based Prediction of Membrane Protein Types using Machine Learning methods

Abstract

Membrane proteins perform an essential protein function, which are cell receptors, channels, and energy transducers. These functions work to keep the survival of the cell. The membrane protein is an integral part of body structure. Therefore, it is necessary to build up machine learning methods to correct identifying membrane protein types to recognize protein function for finding cell life cycle, gene interaction, cell signals, disease occurrence, drug design, and many more. Detection of a membrane protein from sequence length encoding based feature is a key task. The predictor GBDT and Ensemble Adaboost classifiers used to predict a membrane protein, with sequence length, encodes single descriptor value to encodes multilabel feature types. This proposed method has identified the types of the membrane without sequence information loss, and machine Learning classifiers handle the protein datasets well.