使用第三代DNA测序和人工神经网络进行病毒基因组进化的未来方法
Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks
摘要：
第三代DNA测序在过去几年中出现，使用新技术可以产生长读段序列。第三代测序的应用使实时和现场数据生产成为可能，改变了病毒学环境和医学采样的研究范式。为了充分利用长读序测序产生的大规模数据，下游数据分析的创新是必要的。在这里，我们讨论使用机器学习方法来分析大基因数据的未来方法。机器学习结合模式识别和计算学习来执行预测性和探索性数据分析。特别地，深度学习是用于通过人工神经网络解决复杂问题的机器学习领域。与其他方法不同，可以使用神经网络完全从数据中学习特征，而无需手动指定。我们讨论了21世纪病毒学的未来，提出了未来的病毒研究方法，使用实时数据生产和现场数据分析与第三代测序和机器学习方法。我们首先介绍了病毒学中传统统计模型和方法的基本概念，逐步建立了创新下游数据分析的必要性，以满足测序技术的进步。我们认为，人工神经网络可以创新下游数据分析，因为它们可以从大数据集学习，而无需模型假设或特征规范，而不是目前生物信息学中的数据分析。此外，我们讨论了如何使用人工神经网络结合长读序列的未来方法可以彻底改变病毒的研究，在监督和无监督设置的具体例子。
英文摘要：
The Third-Generation in DNA sequencing has emerged in the last few years using new technologies that allow the production of long-read sequences. Applications of the Third-Generation sequencing enable real-time and on-site data production, changing the research paradigms in environmental and medical sampling in virology. To take full advantage of large-scale data generated from long-read sequencing, an innovation in the downstream data analysis is necessary. Here, we discuss futuristic methods using machine learning approaches to analyze big genetic data. Machine learning combines pattern recognition and computational learning to perform predictive and exploratory data analysis. In particular, deep learning is a field of machine learning that is used to solve complex problems through artificial neural networks. Unlike other methods, features can be learned using neural networks entirely from data without manual specifications. We discuss the future of 21st-century virology by presenting futuristic approaches for virus studies using real-time data production and on-site data analysis with the Third-Generation Sequencing and machine learning methods. We first introduce the basic concepts in conventional statistical models and methods in virology, building gradually into the necessity of innovating the downstream data analysis to meet the advances in sequencing technologies. We argue that artificial neural networks can innovate the downstream data analysis, as they can learn from big datasets without model assumptions or feature specifications, as opposed to the current data analysis in bioinformatics. Furthermore, we discuss how futuristic methods using artificial neural networks combined with long-read sequences can revolutionize virus studies, using specific examples in supervised and unsupervised settings.
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