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A linked data approach to discover HPV oncoproteins and RB1 induced mutation associations for the retinoblastoma research

Publication Type: 
Edited Conference Meeting Proceeding
Insight centre for data analytics, National university of Ireland, Galway, Ireland 1 Background: LOSS or GAIN in tumor suppressor gene RB1 play a significant role as in the case of loss low penetrance where only 39% of the eye at risk develops in retinoblastoma. This research covers the multiple mutation types and its effects and identification of the major type of mutation involved in retinoblastoma because of HPV and RB1. Methods: First, we focus on exploring gene expression (GE) patterns for RB1 and HPV-associated genes from TCGA. Second, identification of validated and non-validated standard CNV ensured using the COSMIC. Finally, the clinical profiles of filtered mutations have been validated based on ICGC pathological profiling data to infer the prognostic behavior from RB1 and HPV-associated genes. In order to link and retrieve patterns of a gene from TCGA, COSMIC, and ICGC repositories, we performed following steps: transform heterogeneous data repositories and their storage formats into standard Resource Description Framework (RDF) format; to discover associations by finding specific patterns (i.e. correlations) in the GE data sets; scalable querying the large volume and frequently updating datasets covering the GE data from different repositories
Conference Name: 
Engineering and Physical Sciences in Oncology
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Conference Location: 
United States of America
Research Group: 
National University of Ireland, Galway (NUIG)
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