【PaperReading】Deep Learning for Genomics: A Concise Overview

本文简要探讨了深度学习在基因组学中的应用,从卷积神经网络、循环神经网络和自编码器等角度出发,阐述它们在基因表达、调节基因组学、功能基因组学和结构基因组学等多个领域的应用。同时,文章指出当前面临的挑战,如数据不平衡、多类型数据整合和模型解释性等,并展望了未来的机遇和发展方向。

Deep Learning for Genomics: A Concise Overview

Tianwei Yue, Haohan Wang

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 insight

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