Decision Tree Classifier for Human Protein Function Prediction

Abstract

Drug discoverers need to predict the functions of proteins which are responsible for various diseases in human body. The proposed method is to use priority based packages of SDFs (Sequence Derived Features) so that decision tree may be created by their depth exploration rather than exclusion. This research work develops a new decision tree induction technique in which uncertainty measure is used for best attribute selection. The model creates better decision tree in terms of depth than the existing C4.5 technique. The tree with greater depth ensures more number of tests before functional class assignment and thus results in more accurate predictions than the existing prediction technique. For the same test data, the percentage accuracy of the new HPF (human protein function) predictor is 72% and that of the existing prediction technique is 44%.