Deep learning for computational biology.
Deep learning for computational biology.
复制标题
对计算生物学的深度学习。
DOI：
10.15252/msb.20156651
复制
发表时间：
2016-07-29
影响因子：
9.9
通讯作者：
Stegle O
中科院分区：
文献类型：
--
作者：
Angermueller C;Pärnamaa T;Parts L;Stegle O
基因组学和成像技术的进步导致了来自大量样本的分子和细胞图谱数据的爆炸性增长。生物数据维度和采集率的快速增长对传统的分析策略提出了挑战。现代机器学习方法，如深度学习，承诺利用非常大的数据集来发现其中隐藏的结构，并做出准确的预测。在这篇综述中，我们讨论了这种新的分析方法在调节基因组学和细胞成像中的应用。我们提供了什么是深度学习的背景，以及它可以成功应用于得出生物学见解的背景。除了介绍具体的应用和提供实际使用的提示外，我们还强调了可能的陷阱和限制，以指导计算生物学家何时以及如何最大限度地利用这项新技术。
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.