Title | ||
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Semantic Navigation of Disease-Specific Pathways: The Case of Non-small Cell Lung Cancer (NSCLC). |
Abstract | ||
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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. |
Year | Venue | Field |
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2018 | JIST | Biological data,Disease,Computer science,dbSNP,non-small cell lung cancer (NSCLC),Computational biology,Cancer,RDF,Biological pathway,BioPAX : Biological Pathways Exchange |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 2 |
Name | Order | Citations | PageRank |
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Sung Min Yang | 1 | 0 | 0.34 |
Hong-Gee Kim | 2 | 104 | 18.80 |