Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33992
Title: Applications for Deep Learning in Epilepsy Genetic Research.
Austin Authors: Zeibich, Robert;Kwan, Patrick;J O'Brien, Terence;Perucca, Piero ;Ge, Zongyuan;Anderson, Alison
Affiliation: Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia.
Neurology
Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia.;Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.;Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia.;Epilepsy Research Centre, Department of Medicine, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia.;Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia.
Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia.;Monash-Airdoc Research, Monash University, Melbourne, VIC 3800, Australia.
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia.;Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia.
Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia.;Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia.
Issue Date: 27-Sep-2023
Date: 2023
Publication information: International Journal of Molecular Sciences 2023-09-27; 24(19)
Abstract: Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.
URI: https://ahro.austin.org.au/austinjspui/handle/1/33992
DOI: 10.3390/ijms241914645
ORCID: 0000-0001-6273-7671
0000-0002-5880-8673
Journal: International Journal of Molecular Sciences
PubMed URL: 37834093
ISSN: 1422-0067
Type: Journal Article
Subjects: deep learning
genetic epilepsy
machine learning
non-protein-coding
omics data integration
Appears in Collections:Journal articles

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