Genomic Variant Classifier Tool

Isel Grau, Dipankar Sengupta, Dewan Md Farid, Bernard Manderick, Ann Nowe, Maria M. Garcia Lorenzo, Dorien Daneels, Maryse Bonduelle, Didier Croes, Sonia Van Dooren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identified variant, is still a manual and time consuming process for clinical geneticists. Thus, to facilitate the variant classification process, we have developed GeVaCT, a Java based tool that implements a classification approach based on the literature review of cardiac arrhythmia syndromes. Furthermore, the adoption of this automated knowledge engineer by the clinical geneticists will aid to build a knowledge base for the evolution of the variant classification process by use of novel machine learning approaches.
Original languageEnglish
Title of host publicationProceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
PublisherSpringer, Cham
Pages453-456
Number of pages4
Volume15
ISBN (Electronic)978-3-319-56994-9
ISBN (Print)978-3-319-56993-2
DOIs
Publication statusPublished - 20 Aug 2017

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer, Cham
Volume15
ISSN (Print)2367-3370

Fingerprint

Exome
Inborn Genetic Diseases
Knowledge Bases
Cardiac Arrhythmias
Genome
Machine Learning

Cite this

Grau, I., Sengupta, D., Farid, D. M., Manderick, B., Nowe, A., Lorenzo, M. M. G., ... Van Dooren, S. (2017). Genomic Variant Classifier Tool. In Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (Vol. 15, pp. 453-456). (Lecture Notes in Networks and Systems; Vol. 15). Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_32
Grau, Isel ; Sengupta, Dipankar ; Farid, Dewan Md ; Manderick, Bernard ; Nowe, Ann ; Lorenzo, Maria M. Garcia ; Daneels, Dorien ; Bonduelle, Maryse ; Croes, Didier ; Van Dooren, Sonia. / Genomic Variant Classifier Tool. Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Vol. 15 Springer, Cham, 2017. pp. 453-456 (Lecture Notes in Networks and Systems).
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title = "Genomic Variant Classifier Tool",
abstract = "The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identified variant, is still a manual and time consuming process for clinical geneticists. Thus, to facilitate the variant classification process, we have developed GeVaCT, a Java based tool that implements a classification approach based on the literature review of cardiac arrhythmia syndromes. Furthermore, the adoption of this automated knowledge engineer by the clinical geneticists will aid to build a knowledge base for the evolution of the variant classification process by use of novel machine learning approaches.",
author = "Isel Grau and Dipankar Sengupta and Farid, {Dewan Md} and Bernard Manderick and Ann Nowe and Lorenzo, {Maria M. Garcia} and Dorien Daneels and Maryse Bonduelle and Didier Croes and {Van Dooren}, Sonia",
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Grau, I, Sengupta, D, Farid, DM, Manderick, B, Nowe, A, Lorenzo, MMG, Daneels, D, Bonduelle, M, Croes, D & Van Dooren, S 2017, Genomic Variant Classifier Tool. in Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. vol. 15, Lecture Notes in Networks and Systems, vol. 15, Springer, Cham, pp. 453-456. https://doi.org/10.1007/978-3-319-56994-9_32

Genomic Variant Classifier Tool. / Grau, Isel; Sengupta, Dipankar; Farid, Dewan Md; Manderick, Bernard; Nowe, Ann ; Lorenzo, Maria M. Garcia; Daneels, Dorien; Bonduelle, Maryse; Croes, Didier; Van Dooren, Sonia.

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Vol. 15 Springer, Cham, 2017. p. 453-456 (Lecture Notes in Networks and Systems; Vol. 15).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Genomic Variant Classifier Tool

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AU - Sengupta, Dipankar

AU - Farid, Dewan Md

AU - Manderick, Bernard

AU - Nowe, Ann

AU - Lorenzo, Maria M. Garcia

AU - Daneels, Dorien

AU - Bonduelle, Maryse

AU - Croes, Didier

AU - Van Dooren, Sonia

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N2 - The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identified variant, is still a manual and time consuming process for clinical geneticists. Thus, to facilitate the variant classification process, we have developed GeVaCT, a Java based tool that implements a classification approach based on the literature review of cardiac arrhythmia syndromes. Furthermore, the adoption of this automated knowledge engineer by the clinical geneticists will aid to build a knowledge base for the evolution of the variant classification process by use of novel machine learning approaches.

AB - The exome or genome based high throughput screening techniques are becoming a definitive criterion in the conventional clinical analysis of the genetic diseases. However, pathogenic classification of an identified variant, is still a manual and time consuming process for clinical geneticists. Thus, to facilitate the variant classification process, we have developed GeVaCT, a Java based tool that implements a classification approach based on the literature review of cardiac arrhythmia syndromes. Furthermore, the adoption of this automated knowledge engineer by the clinical geneticists will aid to build a knowledge base for the evolution of the variant classification process by use of novel machine learning approaches.

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BT - Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

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Grau I, Sengupta D, Farid DM, Manderick B, Nowe A, Lorenzo MMG et al. Genomic Variant Classifier Tool. In Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Vol. 15. Springer, Cham. 2017. p. 453-456. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-319-56994-9_32