Computational biology: deep learning

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

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[18]  Guoying Zhao,et al.  Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[19]  Gustavo Carneiro,et al.  A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..

[20]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

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[27]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

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[35]  David J. Arenillas,et al.  JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles , 2015, Nucleic Acids Res..

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[37]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

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[39]  B. Póczos,et al.  Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks , 2016, bioRxiv.

[40]  Brendan J. Frey,et al.  Classifying and segmenting microscopy images with deep multiple instance learning , 2015, Bioinform..

[41]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[42]  Lior Shamir,et al.  WND-CHARM: Multi-purpose image classification using compound image transforms , 2008, Pattern Recognit. Lett..

[43]  Alexander Mordvintsev,et al.  Inceptionism: Going Deeper into Neural Networks , 2015 .

[44]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.

[45]  Konstantinos Kamnitsas,et al.  Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI , 2016, MICCAI.

[46]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[47]  O. Stegle,et al.  DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2016, Genome Biology.

[48]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[49]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[50]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[51]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[52]  Yu Shiong Wong,et al.  GMFR-CNN : , 2017 .

[53]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[54]  Wei Wang,et al.  A deep learning-based segmentation method for brain tumor in MR images , 2016, 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS).

[55]  Kate B. Cook,et al.  Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity , 2014, Cell.

[56]  David R. Kelley,et al.  Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.

[57]  Yanjun Qi,et al.  DeepChrome: deep-learning for predicting gene expression from histone modifications , 2016, Bioinform..

[58]  Sai-Ho Ling,et al.  An Efficient Diagnosis System for Parkinson's Disease Using Deep Belief Network , 2017 .

[59]  L. Stirling Churchman,et al.  FIDDLE: An integrative deep learning framework for functional genomic data inference , 2016, bioRxiv.

[60]  W. Wasserman,et al.  Genome-wide prediction of cis-regulatory regions using supervised deep learning methods , 2016, BMC Bioinformatics.

[61]  D. Gifford,et al.  Predicting the impact of non-coding variants on DNA methylation , 2016 .

[62]  Anshul Kundaje,et al.  Denoising Genome-wide Histone ChIP-seq with Convolutional Neural Networks , 2016 .

[63]  Nico Karssemeijer,et al.  Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring , 2016, IEEE Transactions on Medical Imaging.

[64]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[65]  Louis-François Handfield,et al.  Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images , 2015, Bioinform..

[66]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[67]  Fabian J. Theis,et al.  Reconstructing cell cycle and disease progression using deep learning , 2017 .

[68]  Jean-Karim Hériché,et al.  Systematic Cell Phenotyping , 2014 .

[69]  Feng Liu,et al.  De novo identification of replication-timing domains in the human genome by deep learning , 2015, Bioinform..

[70]  Jianxing Feng,et al.  Imputation for transcription factor binding predictions based on deep learning , 2017, PLoS Comput. Biol..

[71]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[72]  Siegfried Wahl,et al.  Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.

[73]  Yolanda T. Chong,et al.  Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis , 2015, Cell.

[74]  William Stafford Noble,et al.  Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture , 2017, bioRxiv.

[75]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[76]  Yolanda T. Chong,et al.  Automated analysis of high‐content microscopy data with deep learning , 2017, Molecular systems biology.

[77]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[78]  Daniel Lévy,et al.  Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks , 2016, ArXiv.

[79]  Avanti Shrikumar,et al.  Learning Important Features Through Propagating Activation Differences , 2017, ICML.

[80]  Lubomir M. Hadjiiski,et al.  Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. , 2016, Medical physics.

[81]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[82]  Marios Anthimopoulos,et al.  Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis , 2016, IEEE journal of biomedical and health informatics.

[83]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[84]  Naif Alajlan,et al.  Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..

[85]  Priya Aggarwal,et al.  Classification of Schizophrenia versus normal subjects using deep learning , 2016, ICVGIP '16.

[86]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[87]  Leopold Parts,et al.  Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning , 2016, G3: Genes, Genomes, Genetics.

[88]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[89]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[90]  Heng Li,et al.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data , 2011, Bioinform..

[91]  Hung T. Nguyen,et al.  Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[92]  Jianyang Zeng,et al.  TIDE: predicting translation initiation sites by deep learning , 2017, bioRxiv.

[93]  Remi Torracinta,et al.  Training Genotype Callers with Neural Networks , 2016, bioRxiv.

[94]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[95]  B. Frey,et al.  Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.

[96]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[97]  Ning Chen,et al.  DeepEnhancer: Predicting enhancers by convolutional neural networks , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[98]  Chuan-Yu Chang,et al.  Application of deep learning for recognizing infant cries , 2016, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[99]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[100]  Remi Torracinta,et al.  Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data , 2016, bioRxiv.

[101]  Anna Shcherbina,et al.  Not Just a Black Box: Learning Important Features Through Propagating Activation Differences , 2016, ArXiv.

[102]  Su Ruan,et al.  Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.

[103]  Aaron Y. Lee,et al.  Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration , 2016, bioRxiv.

[104]  Yi Li,et al.  Understanding sequence conservation with deep learning , 2017, bioRxiv.

[105]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[106]  Tomáš Vinař,et al.  DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads , 2016, PloS one.

[107]  Walter F. Stewart,et al.  Doctor AI: Predicting Clinical Events via Recurrent Neural Networks , 2015, MLHC.

[108]  Avanti Shrikumar,et al.  Reverse-complement parameter sharing improves deep learning models for genomics , 2017, bioRxiv.

[109]  Jianyang Zeng,et al.  A deep learning framework for modeling structural features of RNA-binding protein targets , 2015, Nucleic acids research.

[110]  Hong-Bin Shen,et al.  RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach , 2016, BMC Bioinformatics.

[111]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[112]  Wolfgang Huber,et al.  EBImage—an R package for image processing with applications to cellular phenotypes , 2010, Bioinform..

[113]  Fabian J Theis,et al.  Prospective identification of hematopoietic lineage choice by deep learning , 2017, Nature Methods.

[114]  Anne E Carpenter,et al.  Automating Morphological Profiling with Generic Deep Convolutional Networks , 2016, bioRxiv.

[115]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[116]  Nung Kion Lee,et al.  GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction , 2016, CSBio.