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Analysis and Prioritisation of Potential Gene Variants Powered by Semantic Intelligence

Published: 02 January 2021 Publication History

Abstract

Next Generation Sequencing techniques like whole genome, exome etc., generate rich variant annotation outputs from the high-throughput sequencing data. These outputs may contain millions of gene variants depending on the sequencing techniques used and need to be filtered to a set of potential gene variant(s) for a disease of interest. Many systems and models have addressed the problem of prioritising gene variants from annotated variant outputs. There is a need to provide a flexible computational model that integrates the contextual knowledge from diverse sources and provides control of the filtering and prioritisation process to the bio-informaticians. Furthermore, to the best of our knowledge, there has been limited efforts to build an inter-operable, reusable and machine-readable knowledge framework to link identified potential variants for multiple known genetic conditions for the Indian population that can be searched, explored, analysed and applied for future cases with increased accuracy. In this paper, we present Sandhi Gene Variant Analysis (Sandhi GVA) that combines individual gene variant analysis summaries of patients, their clinical data and patient information into rich linked data fostering the application of semantic intelligence in effective filtering and prioritisation of potential gene variants in new cases.

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CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
January 2021
453 pages
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

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  1. gene variants prioritisation
  2. linked data
  3. semantic intelligence

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CODS COMAD 2021
CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
January 2 - 4, 2021
Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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