Semantic Navigation of Disease-Specific Pathways: The Case of Non-small Cell Lung Cancer (NSCLC)

Abstract

By studying the cancer genome, scientists can discover what base changes are causing a cell to become a cancer cell. In addition, cancers and diseases are affected by a series of complex interactions between a multitude of entities such as genes and proteins. Biological pathway analysis became necessary to understand these entities within diverse contexts. In this paper, we propose a framework for researchers to navigate disease-specific pathways. The basic structure of analysis data is BioPAX which is described in RDF and is produced by the Reactome database (biological pathway database). For this framework, we utilize a large scale of biological sources such as Pathway Commons, clinical data, dbSNP, and ClinVar. Especially, we choose non-small cell lung cancer (NSCLC) for case study to demonstrate components of semantic navigation. Furthermore, we generate and analyze non-small cell lung cancer (NSCLC) specific pathways. Our proposed system will help researchers find a point at which they begin their interests. For instance, it can help discover which protein or gene most affect a specific disease or it can aid in integrating different sources of biological information. Moreover, plenty of biological data extended by our system suggests a new perspective for scientists to find a direction of research.