Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia.
Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.
Int J Mol Sci. 2023 Sep 27;24(19):14645. doi: 10.3390/ijms241914645.
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.
癫痫是一组以易发生自发性癫痫发作为特征的脑部疾病。由于测序技术和计算方法的进步，现在已经有 900 多个基因与癫痫有关。用于分析大量基因组数据的工具和方法的开发和优化是一个快速发展的研究领域。深度学习 (DL) 是机器学习 (ML) 的一个子集，它为新的研究策略带来了机会，可以利用这些策略深入了解癫痫患者的基因组风险。DL 被用于解决长读测序技术准确性的局限性，该技术改进了短读方法。能够预测遗传变异功能后果的工具可以在解决关键知识空白方面取得突破，而整合独立但互补数据的方法则可以提高遗传数据的预测能力。我们概述了这些 DL 工具，并讨论了它们如何应用于癫痫研究中遗传数据的分析。
