Isel Grau, Isel Grau, Dipankar Sengupta, Dipankar Sengupta, Dewan Md. Farid, Dewan Md. Farid, Bernard Manderick, Bernard Manderick, Ann Nowe, Ann Nowe, Maria M. Garcia Lorenzo, Maria M. Garcia Lorenzo, Dorien Daneels, Dorien Daneels, Maryse Bonduelle, Maryse Bonduelle, Didier Croes, Didier Croes, Sonia Van Dooren, Sonia Van Dooren
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.
Grau, I, Sengupta, D, Farid, DM, Manderick, B, Nowe, A, Garcia Lorenzo, MM, Daneels, D, Bonduelle, M, Croes, D & Van Dooren, S 2016, Genomic Variant Classifier Tool. in PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. vol. 15, Lecture Notes in Networks and Systems, Springer, pp. 453-456, SAI Intelligent Systems Conference 2016, London, United Kingdom, 21/09/16. https://doi.org/10.1007/978-3-319-56994-9_32
Grau, I., Sengupta, D., Farid, D. M., Manderick, B., Nowe, A., Garcia Lorenzo, M. M., Daneels, D., Bonduelle, M., Croes, D., & Van Dooren, S. (2016). Genomic Variant Classifier Tool. In PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (Vol. 15, pp. 453-456). (Lecture Notes in Networks and Systems). Springer. https://doi.org/10.1007/978-3-319-56994-9_32
@inbook{f1447ff4767f49ca822151e0b38ed18e,
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.",
keywords = "variant classification, automated knowledge engineer, genomic variant, cardiac arrhythmia syndromes",
author = "Isel Grau and Dipankar Sengupta and Farid, \{Dewan Md.\} and Bernard Manderick and Ann Nowe and \{Garcia Lorenzo\}, \{Maria M.\} and Dorien Daneels and Maryse Bonduelle and Didier Croes and \{Van Dooren\}, Sonia",
year = "2016",
month = sep,
day = "22",
doi = "10.1007/978-3-319-56994-9\_32",
language = "English",
isbn = "978-3-319-56993-2",
volume = "15",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "453--456",
booktitle = "PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1",
note = "SAI Intelligent Systems Conference 2016 ; Conference date: 21-09-2016 Through 22-09-2016",
url = "http://saiconference.com/Conferences/IntelliSys2016",
}