Deep learning for computational biology

被引：888

作者：

Angermueller, Christof ^{[1
]}

Parnamaa, Tanel ^{[2
,3
]}

Parts, Leopold ^{[2
,3
]}

Stegle, Oliver ^{[1
]}

机构：

[1] European Bioinformat Inst, European Mol Biol Lab, Wellcome Trust Genome Campus, Cambridge, England

[2] Univ Tartu, Dept Comp Sci, Tartu, Estonia

[3] Wellcome Trust Sanger Inst, Wellcome Trust Genome Campus, Cambridge, England

来源：

MOLECULAR SYSTEMS BIOLOGY | 2016年 / 12卷 / 07期

基金：

欧洲研究理事会; 英国惠康基金;

关键词：

cellular imaging; computational biology; deep learning; machine learning; regulatory genomics; GENE-EXPRESSION VARIATION; NEURAL-NETWORKS; RNA; PERCEPTRON; PREDICTION; ALGORITHM; DNA;

D O I：

10.15252/msb.20156651

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：16

共 128 条

[51]

Glorot X, 2010, P 13 INT C ART INT S, P249

[52]

Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1

[53]

Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947

[54] Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions [J].

Grubert, Fabian ;

Zaugg, Judith B. ;

Kasowski, Maya ;

Ursu, Oana ;

Spacek, Damek V. ;

Martin, Alicia R. ;

Greenside, Peyton ;

Srivas, Rohith ;

Phanstiel, Doug H. ;

Pekowska, Aleksandra ;

Heidari, Nastaran ;

Euskirchen, Ghia ;

Huber, Wolfgang ;

Pritchard, Jonathan K. ;

Bustamante, Carlos D. ;

Steinmetz, Lars M. ;

Kundaje, Anshul ;

Snyder, Michael .

CELL, 2015, 162 (05) :1051-1065

[55]

Hastie T., 2009, The Elements of Statistical Learning: Data Mining, Inference and Prediction, V2, P1

[56] Deep Residual Learning for Image Recognition [J].

He, Kaiming ;

Zhang, Xiangyu ;

Ren, Shaoqing ;

Sun, Jian .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778

[57] Reducing the dimensionality of data with neural networks [J].

Hinton, G. E. ;

Salakhutdinov, R. R. .

SCIENCE, 2006, 313 (5786) :504-507

[58]

Hinton G.E., 2012, Neural networks: Tricks of the trade

[59] Deep Neural Networks for Acoustic Modeling in Speech Recognition [J].

Hinton, Geoffrey ;

Deng, Li ;

Yu, Dong ;

Dahl, George E. ;

Mohamed, Abdel-rahman ;

Jaitly, Navdeep ;

Senior, Andrew ;

Vanhoucke, Vincent ;

Patrick Nguyen ;

Sainath, Tara N. ;

Kingsbury, Brian .

IEEE SIGNAL PROCESSING MAGAZINE, 2012, 29 (06) :82-97

[60] A fast learning algorithm for deep belief nets [J].

Hinton, Geoffrey E. ;

Osindero, Simon ;

Teh, Yee-Whye .

NEURAL COMPUTATION, 2006, 18 (07) :1527-1554

← 1 2 3 4 5 6 7 8 9 10 →