Complexity reduction and visualization tool for RDF knowledge network application in precision medicine

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Precision medicine is one of the most rapidly emerging areas of research and development, crucial for improving patient care, but there is a lack of a comprehensive set of tools that are easy to use for analysis and incorporating genomic data into clinical decision. In our study, we attempt to reconstruct interrelationships among biomarker proteins, diseases and signal transduction pathways for individualized treatments. Towards this, we have developed a suite of tools, which can shed some light on tumorigenesis and genomic changes taking place in individual patient. Firstly, we have developed a visualization and curation tool to resolve the problem of missing information in the KEGG database. This tool helped in curation of KEGG pathways. The curated pathways have been converted into RDF to create a knowledge base network. Secondly, we have developed Complexity Reduction and Visualization (CRV) tool for pathologists, oncologists and other specialists in precision medicine. This tool reduces the complexity of the knowledge network by finding the shortest paths among a set of start genes and end genes. The resulting network will be visualized using d3.js. Such a suite of tools can be applied to answering diverse questions including getting a better understanding of genomics mechanisms that play a role in metastasis vs. nonmetastatic cancers. We have applied this to Lung Adenocarcinoma (LUAD) samples. Our system helps build hypothesis but needs to be further validated with more testing using some benchmark ground truth datasets. In one dataset, we have found 90 genes and 15 pathways like focal adhesion, mTOR and ErbR signaling pathway that have a role in transforming the cancer from non-metastatic cancer to be a metastatic cancer.