Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

By Babak Alipanahi, Andrew Delong, Matthew T. Weirauch, Brendan J. Frey. 2015

2,712 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Computational Biology & Bioinformatics

Citations in the corpus

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “…learning applications with great beneﬁts are enabled only through the analysis of sensitive data, such as users’ personal contacts, private photographs or correspondence, or even medical records or genetic sequences (Alipanahi et al., 2015; Kannan et al., 2016; Kononenko, 2001; Sweeney, 1997).”
Split learning for health: Distributed deep learning without sharing raw patient data (2018, background): “Deep learning methods in general have found a pervasive suite of applications in biology, clinical medicine, genomics and public health as surveyed in [24, 23, 25, 29, 30, 31].”
Methods for interpreting and understanding deep neural networks (2017, positive): “[1] trained a convolutional neural network to map the DNA sequence to protein binding sites.”
Learning to predict the cosmological structure formation (2018, background): “This new paradigm is also significantly impacting a variety of domains in the sciences, from biology (24, 25) to chemistry (26, 27) and physics (28, 29).”
Geometric Deep Learning: Going beyond Euclidean data (2016, background): “graphics and vision [26], [27], [28], natural language processing [29], and biology [30], [31].”
Prior Networks for Detection of Adversarial Attacks (2018, background): “Neural Networks (NNs) have become the dominant approach to addressing computer vision (CV) [1, 2, 3], natural language processing (NLP) [4, 5, 6], speech recognition (ASR) [7, 8] and bioinformatics [9, 10] tasks.”
Predictive Uncertainty Estimation via Prior Networks (2018, background): “Neural Networks (NNs) have become the dominant approach to addressing computer vision (CV) [1, 2, 3], natural language processing (NLP) [4, 5, 6], speech recognition (ASR) [7, 8] and bio-informatics (BI) [9, 10] tasks.”
Training GANs with Optimism (2017, methodology): “Both the discriminator and generator of the WGAN used in this analysis were chosen to be convolutional neural networks (CNN), given the recent success of CNNs in modeling DNA-protein binding (Zeng et al., 2016; Alipanahi et al., 2015).”
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations (2017, background): “Introduction Deep learning has gained unprecedented attention over the last few years, and deservedly so, as it has introduced transformative solutions across diverse scientific disciplines [1, 2, 3, 4].”
Mean Field Analysis of Neural Networks: A Law of Large Numbers (2018, methodology): “Neural networks have been used to identify cancer [17] and to model protein folding [1].”
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks (2018, background): “recognition [1], natural language processing [2], cognitive science [3], and genomics [4].”
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations (2017, background): “Introduction With the explosive growth of available data and computing resources, recent advances in machine learning and data analytics have yielded transformative results across diverse scientific disciplines, including image recognition [1], natural language processing [2], cognitive science [3],”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, neutral): “Deep learning was recently applied to medicine and genomics to reconstruct brain circuits 35 and to predict the activity of potential drug molecules 36 , the effects of mutations in non-coding DNA on gene expressions 37,38 , and the sequence specificities of DNA and RNA-binding proteins 39 .”
ProteinNet: a standardized data set for machine learning of protein structure (2019, background): “Background Deep learning has revolutionized many areas of computer science including computer vision, natural language processing, and speech recognition [1], and is now being widely applied to bioinformatic problems ranging from clinical image classification [2] to prediction of protein-DNA binding”
Biological Sequence Modeling with Convolutional Kernel Networks (2017, positive): “…protein domains into superfamilies [Leslie et al., 2003, Saigo et al., 2004], predicting whether a DNA or RNA sequence binds to a protein [Alipanahi et al., 2015], its splicing outcome [Jha et al., 2017], or its chromatin accessibility [Kelley et al., 2016], predicting the resistance of…”
Likelihood Ratios for Out-of-Distribution Detection (2019, methodology): “For classifier-based baselines, we construct CNNs with one convolutional layer, one max-pooling layer, one dense layer, and a final dense layer with softmax activation for predicting class probabilities, as in Alipanahi et al. (2015); Busia et al.”
Deep Learning for Health Informatics (2017, background): “In translational bioinformatics, such features may also determine nucleotide sequences that could bind a DNA or RNA strand to a protein [3].”