Risk Assessment and Drug Design Using R

Abstract

Background: Bioinformatics handles living organism data and inspects the data using computer science facilities. Emerging modern bioinformatics tools associated with high technologies reveal a new area of drug design, it is now possible to drug design structurally. Several research works have revealed the ways of how a drug can be designed using bioinformatics techniques and tools. Objective: More than an era people are being killed by Heart diseases globally. Heart disease is mainly known as Cardiovascular disease (CVDs). CVDs is the leading reason for universal premature deaths. Stroke and Myocardial infarction are standing at peak position among several CVDs in the world. Types of CVDs cover all existent frequently occurring heart diseases. CVDs are increasing because of common risk factors gradually. Common risk factor diseases have genetic association indirectly or directly. A disease is an abnormal condition in a single gene that affects body negatively. Biomolecule or protein is the best key for structure-based drug design. Data mining as well as data analysis is essential in bioinformatics to find the desired data. Results: Using Knowledge discovery in database (KDD) the process of data mining, genes are filtered, preprocessed, transformed and mined to bring out common gene among 5 types of CVDs. Protein-protein interactions (PPI) are generated from common gene to visualize protein interactions and further evaluations. Conclusions: This study claimed to design a common pathway drug for all types of CVDs. The Genes associated with CVDs types are collected from NCBI database using R. To achieve the goal UniHi is used as a tool.