国家重点研发计划(2016YFA0501403, 2017YFC0906703), 国家自然科学基金(21675172)和蛋白质组学国家重点实验室自主课题(SKLP-K201706)资助项目
This work was supported by grants from National Key Program for Basic Research of China (2016YFA0501403, 2017YFC0906703), The National Natural Science Foundation of China (21675172) and National Key Laboratory of Proteomics Grant (SKLP-K201706)
深度学习是近年来机器学习领域最热门的研究方向,尤其是在图像及语音识别、自然语言处理、自动驾驶等方面取得了突破性进展.生物质谱是当今生命科学领域重要的研究工具,尤其在蛋白质组学、代谢组学、生物制药等领域发挥着关键作用.近年来,基于深度学习方法的发展,以生物质谱为核心的蛋白质组学大数据分析将迎来发展新契机.本文综述了深度学习方法在生物质谱数据解析及蛋白质组学研究方面的最新应用.
Deep learning is the most popular research area in the field of machine learning in recent years, especially in image and speech recognition, natural language processing, and automatic driving.Biological mass spectrometry is an important research tool in the field of life sciences and plays a key role in proteomics, metabolomics, and biopharmaceuticals.In recent years, based on the development of deep learning methods, the big data analysis in proteomics centered on biological mass spectrometry will usher into a new era.This article reviews the latest applications of deep learning methods in the analysis of biological mass spectrometry data and proteomics research.
赵新元,秦伟捷,钱小红.深度学习方法在生物质谱及蛋白质组学中的应用[J].生物化学与生物物理进展,2018,45(12):1214-1223
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