Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.
Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.
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DOI：
10.3389/fnagi.2022.1027224
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发表时间：
2022
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
确定非编码基因变异如何导致神经退行性痴呆是了解疾病发病机制、改善患者预后和开发新的临床治疗方法的基础。下一代测序技术已经产生了大量关于细胞类型特定的转录因子结合、基因表达和三维染色质相互作用的基因组数据，有望为疾病的生物学机制提供关键的见解。然而，这些数据非常复杂，这给研究人员解释、吸收和剖析带来了挑战。为此，深度学习已成为基因组分析的强大工具，可以捕获这些大型数据集中的复杂模式和依赖关系。在这篇综述中，我们整理和讨论了过去几年出现的许多独特的模型架构、开发理念和解释方法，重点是使用深度学习来预测遗传变异对疾病发病机制的影响。我们强调广泛适用的基因组深度学习方法，这些方法可以微调到疾病特定的背景，以及现有的神经退行性疾病研究，重点是阿尔茨海默氏症的特定文献。最后，我们概述了神经退行性变、基因组学和深度学习领域的未来。
Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer’s-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
影响因子：
4.8
作者：
Winick-Ng W;Rylett RJ
通讯作者：
Rylett RJ