Function Prediction of Human Proteins

Abstract

Due to the rapidness in research, accumulation of biological data is happening at an overwhelming rate. Advanced computation techniques are required to gather the useful information from this enormous amount of protein data such that the knowledge is practically useful and easily interpretable. For instance, drug discoverers need biological or computational methods to predict the functions of proteins, responsible for different sort of diseases in human body. Since traditional biological methods were time consuming and comparatively expensive, various computational methods have been introduced in the respective research areas. In this project, we have tried to generate machine learning models that predict the protein function of unknown proteins and analyze their performance to get a model with highest accuracy. Protein function's sequence annotations such as Amino Acid modifications, Molecule Processing and other structural features like Active Site, Beta strand, Chain, etc. along with it even protein mass and length are considered for prediction of protein functions. To further improve the accuracy feature selection has been performed. According to the enzyme nomenclature scheme the protein are classified into 6 groups. This enzyme classes is nothing but the crystalize reactions of proteins and shows the functions of it.