Semantic Navigation of Disease-Specific Pathways: The Case of Non-small Cell Lung Cancer (NSCLC)
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.
KeywordsPathway analysis Biomedical semantics Network analysis
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2017-0-01630) supervised by the IITP (Institute for Information & communications Technology Promotion).
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