Opportunities and obstacles for deep learning in biology and medicine.
Opportunities and obstacles for deep learning in biology and medicine.
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DOI：
10.1098/rsif.2017.0387
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发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Greene CS
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文献类型：
--
作者：
Ching T;Himmelstein DS;Beaulieu-Jones BK;Kalinin AA;Do BT;Way GP;Ferrero E;Agapow PM;Zietz M;Hoffman MM;Xie W;Rosen GL;Lengerich BJ;Israeli J;Lanchantin J;Woloszynek S;Carpenter AE;Shrikumar A;Xu J;Cofer EM;Lavender CA;Turaga SC;Alexandari AM;Lu Z;Harris DJ;DeCaprio D;Qi Y;Kundaje A;Peng Y;Wiley LK;Segler MHS;Boca SM;Swamidass SJ;Huang A;Gitter A;Greene CS
深度学习描述了一类能够将原始输入组合成中间特征层的机器学习算法。这些算法最近在各个领域显示出令人印象深刻的结果。生物学和医学是数据丰富的学科，但这些数据很复杂，而且往往难以理解。因此，深度学习技术可能特别适合解决这些领域的问题。我们研究了深度学习在各种生物医学问题上的应用——患者分类、基本生物学过程和患者治疗——并讨论了深度学习是否能够改变这些任务，或者生物医学领域是否面临独特的挑战。经过广泛的文献回顾，我们发现深度学习尚未彻底改变生物医学或明确解决该领域任何最紧迫的挑战，但在现有技术的基础上已经取得了可喜的进展。尽管总体上比以前的基线有所改善，但最近的进展表明，深度学习方法将为加速或协助人类调查提供有价值的手段。尽管将特定神经网络的预测与输入特征联系起来已经取得了进展，但理解用户应该如何解释这些模型，以对正在研究的系统做出可测试的假设，仍然是一个开放的挑战。此外，用于培训的标记数据数量有限，在某些领域带来了问题，涉及敏感健康记录的工作也受到法律和隐私方面的限制。尽管如此，我们预计深度学习将在实验室和病床上带来变化，并有可能改变生物学和医学的几个领域。
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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DOI：
10.1093/bioinformatics/btt389
发表时间：
2013-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ames SK;Hysom DA;Gardner SN;Lloyd GS;Gokhale MB;Allen JE
通讯作者：
Allen JE
影响因子：
4.3
作者：
Agius, Phaedra;Arvey, Aaron;Leslie, Christina
通讯作者：
Leslie, Christina
影响因子：
4.4
作者：
Abramoff, Michael David;Lou, Yiyue;Niemeijer, Meindert
通讯作者：
Niemeijer, Meindert
影响因子：
5.8
作者：
Andreatta, Massimo;Nielsen, Morten
通讯作者：
Nielsen, Morten
影响因子：
12.3
作者：
Angermueller C;Lee HJ;Reik W;Stegle O
通讯作者：
Stegle O