An Intelligent Computational Model To Predict Target Genes For Infectious Disease

Biological databases consist of variety of information such as the interactions between proteins, biological functions of proteins and the structure of the proteins. Most of the biological functions of the human body are coordinated by the interactions of proteins with other proteins. Proteins don’t act alone. It interacts with other proteins in a biological network to perform numerous functions such as signaling, gene expression. Most of the biological functions are performed with the help of protein interaction network. There is a great need to understand the protein interaction network that helps the researcher to identify proteins associated for various diseases. Many intelligent based computational based approaches have been analyzed by the researchers to improve the prediction exactness of the vital proteins for diseases. However, it is challenging to predict the vital proteins associated with diseases. In this research, we outline the traditional data mining and machine learning methods available to predict the proteins associated with the disease, projecting the strengths, weaknesses and challenges of each model. Furthermore, a new research direction to mine the most vital proteins is proposed and results are remarkable than traditional methods