期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：8
出版社：Journal of Theoretical and Applied
摘要：The identification of target proteins for diseased condition yields the development of the disease detection recommender system and drug discovery processes whose reticence can demolish the pathogen. The testing of this drug discovery is done through clinical and in addition through pre-clinical observations first on the creatures then on people. Thereafter the discovered drug is ready for public use. But if the drug discovery testing phase does not show the suitable consequences, then the entire task must be repeated. This repetitive clinical as well as the preclinical experimentation task is very cumbersome. But keeping in view the importance of the disease detection and drug discovery phase in protein identification as well as in the protein classification process this task must be done by researchers. The advancements in computational biology reveal the importance of computational prediction of protein function or to identify the target on the basis of protein sequence extracted features. To accurately predict the human protein functionalities, lots of approaches are incorporated but this is a very cumbersome task due to the large and versatile nature of the domain. The present work will help to do this job through computational prediction. This paper involves the development of a model which use associative rule mining to extract the sequence derived features at a single platform (SDFES-Sequence derived feature extraction server) from the given human protein sequence and then critically analyzed with machine learning (ML) approaches under the aegis of data analysis tool WEKA. The new sequence derived features are identified and incorporated in the data set, and the scopes of ML approaches were examined for effective prediction. The important configuration incorporation and their configured comparison of approaches are completed to accomplish higher accuracy. In addition to comparative analysis, the limitation of ML approach is discussed along with its remedies by changing the configurations. The proposed work will assist to derive the sequence extracted feature together at a single place and further predict the class or function of the protein which leads to the innovation in drug discovery and disease detection recommender systems.
关键词：Protein; Machine Learning; WEKA; Random Forest; Decision Tree.
