Research On Construction Of Disease,Gene Variant And Drug Relation Knowledge Base

Due to the advancement of platform-independence and worldwide accessibility,the popularity of the Web-based information systems leads to countless Web applications.In the course of this work,a possibility of Web-based information system applications based on searching disease,Gene Variant,and Drug relation is introduced.We have constructed a prototype knowledge base in form of a web application for searching disease,Gene Variant,and Drug relation using Pharm GKB data and Comparative Toxicogenomics Database.The primary aim of the designed web application is to allow the user to conveniently query information view of relationship for the drug,disease,and gene after searching using disease name,drug name,or gene symbol.The My SQL database with 10 tables was created,using the PHP-HTML scripts;the data were captured from the database and presented the information on the web interface.The relation is presented in tabular text and network.The rule of presenting the relation in tabular is different from the one used in the network.The tabular considered the presentation of every available searched query from the database,the searched results presented if it is related or not related to another item from the database.The network presentation from Cytoscape Web plugin using JQuery scripts file has been included for smart visualization.The network is presented only if the searched query is related to two different queries;i.e,if searched query is disease for network to be drawn,must be related to drug and gene.The created database has got 492380 relationships for diseases,variant gene,and drug.Further steps were taken to analyze the available data using igraph in R programming and present visualization using Cytoscape software.Using the random network models,the network data analysis was performed to link drugs,diseases,and genes relation.This method is used to assist in disease diagnosis,treatments,and drug discovery.The network characteristics show that node degree in most of the sub-networks follows approximately a power-law distribution.In centralities,the results indicate that if genes in the Gene-Gene Network occupy important topological positions,then their associated drugs and diseases always hold critical roles in the Drug-Drug Network and the Disease-Disease Network,respectively.In the future work,the numbers of data from different databases will be gradually increased by mapping the disease ontology to reflect the new developments and advancements in the associated fields.