深度学习在基因组学中的应用：简明综述
Deep Learning for Genomics: A Concise Overview
AI总结：
该综述针对基因组学大数据对传统方法的挑战，从基因组学视角分析不同深度学习模型的适配性与开发实践，梳理其在基因组研究中的应用，指明未来发展的机遇与障碍。
摘要：
高通量测序技术等基因组研究进展推动现代基因组研究进入“大数据”学科范畴，数据量的爆发式增长持续对基因组学中的传统方法构成挑战。在对鲁棒算法的迫切需求之下，深度学习已在视觉、语音、文本处理等诸多领域取得成功。然而基因组学为深度学习带来了独特挑战：我们期望深度学习具备超越人类现有知识的超人智能，以实现基因组的解读，而性能强大的深度学习模型需依托对任务特定知识的深刻洞察与运用。本文从基因组学视角简要探讨了不同深度学习模型的优势，旨在为各类特定任务匹配适宜的深度架构，并阐述了开发现代基因组学深度学习架构的实践考量。我们还对深度学习在基因组研究各方面的应用进行了简明综述，同时指出了未来基因组学应用中的潜在机遇与障碍。
英文摘要：
Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
