Deep Learning and Its Applications in Biomedicine
Tài liệu tham khảo
Yu, 2011, Deep learning and its applications to signal and information processing, IEEE Signal Process Mag, 28, 145, 10.1109/MSP.2010.939038
Fukushima, 1980, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol Cybern, 36, 193, 10.1007/BF00344251
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput, 18, 1527, 10.1162/neco.2006.18.7.1527
Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647
Cios, 2005, Computational intelligence in solving bioinformatics problems, Artif Intell Med, 35, 1, 10.1016/j.artmed.2005.07.001
Längkvist, 2014, A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognit Lett, 42, 11, 10.1016/j.patrec.2014.01.008
Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv Neural Inform Process Syst, 60, 1097
Asgari E, Mofrad MRK. ProtVec: a continuous distributed representation of biological sequences. arXiv1503.05140v1.
Hubel, 1962, Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex, J Physiol, 160, 106, 10.1113/jphysiol.1962.sp006837
Hubel, 1959, Receptive fields of single neurones in the cat’s striate cortex, J Physiol, 148, 574, 10.1113/jphysiol.1959.sp006308
Weng, 1992, Cresceptron: a self-organizing neural network which grows adaptively, Proc Int Jt Conf Neural Netw, 1, 576
Weng, 1993, Learning recognition and segmentation of 3-D objects from 2-D images, Proc IEEE Int Conf Comput Vis, 121
Weng, 1997, Learning recognition and segmentation using the cresceptron, Int J Comput Vis, 25, 109, 10.1023/A:1007967800668
Riesenhuber, 1999, Hierarchical models of object recognition in cortex, Nat Neurosci, 2, 1019, 10.1038/14819
Joseph, 1961
Viglione, 1970, Applications of pattern recognition technology, 115, 10.1016/S0076-5392(08)60492-0
Newell, 1969, Perceptrons An introduction to computational geometry, Science, 165, 780, 10.1126/science.165.3895.780
Werbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences. In: Ph.D. dissertation, Harvard University; 1974, 29:65−78.
Werbos, 1982, Applications of advances in nonlinear sensitivity analysis, 762
Werbos, 2006, Backwards differentiation in ad and neural nets: past links and new opportunities, 15
LeCun, 1985, Une procédure d’apprentissage pour réseau à seuil asymétrique, Proc Cogn, 599
LeCun Y. A theoretical framework for back-propagation. Proc 1988 Connect Model Summer Sch 1988:21–8.
Lang, 1990, A time-delay neural network architecture for isolated word recognition, Neural Netw, 3, 23, 10.1016/0893-6080(90)90044-L
Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Netw, 61, 85, 10.1016/j.neunet.2014.09.003
Rumelhart DE, McClelland JL, the PDP Research Group. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge: MIT Press; 1986, p. 318–62.
West AHL, Saad D. Adaptive back-propagation in on-line learning of multilayer networks. NIPS'95 Proc 8th Int Conf Neural Inform Process Syst 1995:323–9.
Battiti, 1989, Accelerated backpropagation learning: two optimization methods, Complex Syst, 3, 331
Almeida, 1990
Marquardt, 1963, An algorithm for least-squares estimation of nonlinear parameters, J Soc Ind Appl Math, 11, 431, 10.1137/0111030
Gauss, 1809
Broyden, 1965, A class of methods for solving nonlinear simultaneous equations, Math Comput, 19, 577, 10.1090/S0025-5718-1965-0198670-6
Fletcher, 1963, A rapidly convergent descent method for minimization, Comput J, 6, 163, 10.1093/comjnl/6.2.163
Goldfarb, 1970, A family of variable-metric methods derived by variational means, Math Comput, 24, 23, 10.1090/S0025-5718-1970-0258249-6
Shanno, 1970, Conditioning of quasi-Newton methods for function minimization, Math Comput, 24, 647, 10.1090/S0025-5718-1970-0274029-X
Møller M. Exact calculation of the product of the hessian matrix of feed-forward network error functions and a vector in 0 (n) time. Daimi Rep 1993:14.
Hestenes, 1952, Methods of conjugate gradients for solving linear systems, J Res Nat Bur Stand, 49, 409, 10.6028/jres.049.044
Cortes, 1995, Support-vector networks, Mach Learn, 20, 273, 10.1007/BF00994018
Ho TK. Random decision forests. Proc 3rd Int Conf Doc Anal Recognit 1995;1:278–82.
Ho, 1998, The random subspace method for constructing decision forest, IEEE Trans Pattern Anal Mach Intell, 20, 832, 10.1109/34.709601
Altman, 1992, An introduction to kernel and nearest-neighbor nonparametric regression, Am Stat, 46, 175
Graves, 2011, Practical variational inference for neural networks, 2348
Bengio, 1994, Learning long-term dependencies with gradient descent is difficult, IEEE Trans Neural Netw Learn Syst, 5, 157, 10.1109/72.279181
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J. Flexible, high performance convolutional neural networks for image classification. IJCAI'11 Proc 22ed Int Joint Conf Artif Intell 2011;22:1237–42.
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, Signal Process Mag IEEE, 29, 82, 10.1109/MSP.2012.2205597
Cireşan, 2010, Deep, big, simple neural nets for handwritten digit recognition, Neural Comput, 22, 3207, 10.1162/NECO_a_00052
Raina R, Madhavan A, Ng AY. Large-scale deep unsupervised learning using graphics processors. ICML'09 Proc 26th Ann Int Conf Mach Learn 2009:873–80.
Hinton, 2007, Boltzmann machine, Scholarpedia, 2, 1668, 10.4249/scholarpedia.1668
Bengio, 2009, 1
Sutskever, 2007, Learning multilevel distributed representations for high-dimensional sequences, J Mach Learn Res, 2, 548
Sarikaya, 2014, Application of deep belief networks for natural language understanding, IEEE/ACM Trans Audio Speech Lang Process, 22, 778, 10.1109/TASLP.2014.2303296
Matsugu, 2003, Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Netw, 16, 555, 10.1016/S0893-6080(03)00115-1
Sermanet, 2011, Traffic sign recognition with multi-scale convolutional networks, Neural Netw, 42, 3809
Lawrence, 1997, Face recognition: a convolutional neural-network approach, IEEE Trans Neural Netw Learn Syst, 8, 98, 10.1109/72.554195
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. ProcIEEEComput Soc Conf Comput VisPatternRecognit. 2015:1−9.
Long, 2015, Fully convolutional networks for semantic segmentation, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 79, 3431
Karpathy, 2014, Large-scale video classification with convolutional neural networks, Proc IEEE Conf Comput Vis Pattern Recognit, 1725
Simonyan, 2014, Two-stream convolutional networks for action recognition in videos, 568
Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. ACM Proc Int Conf Mach Learn; 2008:160–7.
Hochreiter, 1997, Long short-term memory, Neural Comput, 9, 1735, 10.1162/neco.1997.9.8.1735
Graves, 2012
Goodfellow, 2015, Modern practical deep networks, 162
Gers, 2001, LSTM recurrent networks learn simple context-free and context-sensitive languages, IEEE Trans Neural Netw, 12, 1333, 10.1109/72.963769
Graves, 2009, Offline handwriting recognition with multidimensional recurrent neural networks, 545
Ballard, 1987, Modular learning in neural networks, Proc Conf AAAI Artif Intell, 279
Schölkopf, 2007, Greedy layer-wise training of deep networks, Adv Neural Inf Process Syst, 153
Schölkopf, 2007, Efficient sparse coding algorithms, Adv Neural Inf Process Syst, 801
Bengio, 2012, Practical recommendations for gradient-based training of deep architectures, Lect Notes Comput Sci, 7700, 437, 10.1007/978-3-642-35289-8_26
Singh, 2013, The impact of transformation function on the classification ability of complex valued extreme learning machines, Int Conf Control Comput Commun Mater, 1
Toth, 2013, Phone recognition with deep sparse rectifier neural networks, Proc IEEE Int Conf Acoust Speech Signal Process, 6985
Maas AL, Hannun AY, Ng AY. Rectifier nonlinearities improve neural network acoustic models. Proc 30thInt Conf Mach Learn 2013:30.
Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. ICML'10 Proc 27thInt Conf Mach Learn 2010:807–14.
Lai M. Deep learning for medical image segmentation. arXiv150502000.
Glorot, 2011, Deep sparse rectifier neural networks, J Mach Learn Res, 15, 315
Jarrett, 2009, What is the best multi-stage architecture for object recognition?, Proc IEEE Int Conf Comput Vis, 2146
Goodfellow IJ, Warde-Farley D, Mirza M, Courville A. Maxout Networks. arXiv13024389.
Rosasco, 2004, Are loss functions all the same?, Neural Comput, 16, 1063, 10.1162/089976604773135104
Binmore, 2002
Boyd, 2004
Huang, 2011, A new method of regularization parameter estimation for source localization, IEEE CIE Int Conf, 2, 1804
Yu, 2002, Rank/norm regularization with closed-form solutions: application to subspace clustering, Assoc Uncertain Artif Intell, 1
Abernethy, 2009, A new approach to collaborative filtering: operator estimation with spectral regularization, J Mach Learn Res, 10, 803
Argyriou, 2008, Convex multi-task feature learning, Mach Learn, 73, 243, 10.1007/s10994-007-5040-8
Obozinski, 2010, Joint covariate selection and joint subspace selection for multiple classification problems, Stat Comput, 20, 231, 10.1007/s11222-008-9111-x
Gauriau, 2015, Multi-organ localization with cascaded global-to-local regression and shape prior, Med Image Anal, 23, 70, 10.1016/j.media.2015.04.007
Bottou L. Stochastic gradient learning in neural networks. Proc Neuro Nımes 1991;91.
Lecun, 1998, Gradient-based learning applied to document recognition, Proc IEEE, 86, 2278, 10.1109/5.726791
Zinkevich, 2010, Parallelized stochastic gradient descent, 2595
Hinton, 1999, Products of experts. ICANN, 1
Hinton, 2002, Training products of experts by contrastive divergence, Neural Comput, 1771, 10.1162/089976602760128018
Carreira-Perpinan, 2005, On contrastive divergence learning, Proc Artif Intell Stat, 1
Jim, 1996, An analysis of noise in recurrent neural networks: convergence and generalization, IEEE Trans Neural Netw, 7, 1424, 10.1109/72.548170
Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
Lasserre, 2006, Principled hybrids of generative and discriminative models, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 1, 87
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. arXiv12070580.
Srivastava, 2014, Dropout : a simple way to prevent neural networks from overfitting, J Mach Learn Res, 15, 1929
Aurelio Ranzato, 2007, Efficient learning of sparse representations with an energy-based model, 1137
Bourlard, 1988, Auto-association by multilayer perceptrons and singular value decomposition, Biol Cybern, 59, 291, 10.1007/BF00332918
Hinton, 2012, A practical guide to training restricted boltzmann machines, 599
Hinton, 2009, 5947
Erhan, 2010, Why does unsupervised pre-training help deep learning?, J Mach Learn Res, 11, 625
Ciresan, 2012, Multi-column deep neural networks for image classification, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 3642
Werbos, 1988, Generalization of backpropagation with application to a recurrent gas market model, Neural Netw, 1, 339, 10.1016/0893-6080(88)90007-X
Pearlmutter, 2011, Learning state space trajectories in recurrent neural networks, Neural Comput, 1, 263, 10.1162/neco.1989.1.2.263
Hochreiter, 2001, Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 237
Syed, 1995
Gomez, 2008, Accelerated neural evolution through cooperatively coevolved synapses, J Mach Learn Res, 9, 937
Pereira, 2016, Brain Tumor segmentation using convolutional neural networks in MRI images, IEEE Trans Med Imaging, 35, 1240, 10.1109/TMI.2016.2538465
Havaei, 2017, Brain tumor segmentation with deep neural networks, Med Image Anal, 35, 18, 10.1016/j.media.2016.05.004
Moreira, 2012, INbreast: Toward a full-field digital mammographic database, Acad Radiol, 19, 236, 10.1016/j.acra.2011.09.014
Health, 2001, The digital database for screening mammography, 457
Ngo, 2017, Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance, Med Image Anal, 35, 159, 10.1016/j.media.2016.05.009
Roth HR, Farag A, Lu L, Turkbey EB, Summers RM. Deep convolutional networks for pancreas segmentation in CT imaging. ArXiv1504.03967.
Prasoon, 2013, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, Med Image Comput Comput Assist Interv, 8150, 246
Liao, 2013, Representation learning: A unified deep learning framework for automatic prostate MR segmentation, Med Image Comput Comput Assist Interv, 16, 254
Guo, 2014, Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features, Med Image Comput Comput Assist Interv, 8674, 308
Kim, 2013, Unsupervised deep learning for hippocampus segmentation in 7.0 tesla MR images, 1
Schlegl, 2015, 437
Xu Y, Li Y, Liu M, Wang Y, Fan Y, Lai M, et al. Gland instance segmentation by deep multichannel neural networks. arXiv160704889.
Lerouge, 2015, IODA: an input/output deep architecture for image labeling, Pattern Recognit, 48, 2847, 10.1016/j.patcog.2015.03.017
Moeskops, 2016, Automatic segmentation of MR brain images with a convolutional neural network, IEEE Trans Med Imaging, 35, 1252, 10.1109/TMI.2016.2548501
Shin, 2013, Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data, IEEE Trans Pattern Anal Mach Intell, 35, 1930, 10.1109/TPAMI.2012.277
Roth, 2015, Anatomy-specific classification of medical images using deep convolutional nets, Proc IEEE Int Symp Biomed Imaging, 101
Sheet, 2015, Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology, Proc IEEE Int Symp Biomed Imaging, 777
Dou, 2016, Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks, IEEE Trans Med Imaging, 35, 1182, 10.1109/TMI.2016.2528129
Wolterink, 2016, Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks, Med Image Anal, 34, 123, 10.1016/j.media.2016.04.004
Zhou, 2015, A comparative study of two prediction models for brain tumor progression, Image Process Algorithms Syst, 9399
Tran, 2013, High-dimensional MRI data analysis using a large-scale manifold learning approach, Mach Vis Appl, 24, 995, 10.1007/s00138-013-0499-8
Sirinukunwattana, 2016, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans Med Imaging, 35, 1196, 10.1109/TMI.2016.2525803
Xu, 2012, Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 964
Cireşan, 2013, Mitosis detection in breast cancer histology images with deep neural networks, Med Image Comput Comput Assist Interv, 411
Cruz-Roa, 2014, Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks, Med Imaging, 9041
Kooi, 2017, Large scale deep learning for computer aided detection of mammographic lesions, Med Image Anal, 35, 303, 10.1016/j.media.2016.07.007
Kallenberg, 2016, Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring, IEEE Trans Med Imaging, 35, 1322, 10.1109/TMI.2016.2532122
Srivastava, 2015, Using deep learning for robustness to parapapillary atrophy in optic disc segmentation, IEEE 12th Int Symp Biomed Imaging, 768
Fang, 2015, Retinal vessel landmark detection using deep learning and hessian matrix, Proc Int Symp Image Signal Process Anal, 387
Van Grinsven, 2016, Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images, IEEE Trans Med Imaging, 35, 1273, 10.1109/TMI.2016.2526689
Prentašić, 2015, Detection of exudates in fundus photographs using convolutional neural networks, Proc Int Symp Image Signal Process Anal, 188
Arunkumar, 2015, Multi-retinal disease classification by reduced deep learning features, Neural Comput Appl, 1
Mirowski, 2008, Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG, IEEE Int Workshop Mach Learn Signal Process, 244
Mirowski, 2007, Time-delay neural networks and independent component analysis for Eeg-Based prediction of epileptic seizures propagation, Proc Conf AAAI Artif Intell, 1892
Mirowski, 2009, Classification of patterns of EEG synchronization for seizure prediction, Clin Neurophysiol, 120, 1927, 10.1016/j.clinph.2009.09.002
Davidson, 2007, EEG-based lapse detection with high temporal resolution, IEEE Trans Biomed Eng, 54, 832, 10.1109/TBME.2007.893452
Petrosian, 2000, Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG, Neurocomputing, 30, 201, 10.1016/S0925-2312(99)00126-5
Chen, 2016, Gene expression inference with deep learning, Bioinfarmatics, 32, 1832, 10.1093/bioinformatics/btw074
Zhang, 2016, A deep learning framework for modeling structural features of RNA-binding protein targets, Nucleic Acids Res, 44, e32, 10.1093/nar/gkv1025
Alipanahi, 2015, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nat Biotechnol, 33, 1, 10.1038/nbt.3300
Lanchantin J, Singh R, Lin Z, Qi Y. Deep Motif: visualizing genomic sequence classifications. arXiv160501133.
Zeng, 2016, Convolutional neural network architectures for predicting DNA-protein binding, Bioinformatics, 32, i121, 10.1093/bioinformatics/btw255
Kelley, 2016, Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks, Genome Res, 26, 990, 10.1101/gr.200535.115
Liu, 2015, De novo identification of replication-timing domains in the human genome by deep learning, Bioinformatics, 32, 641, 10.1093/bioinformatics/btv643
Liu, 2016, PEDLA: predicting enhancers with a deep learning-based algorithmic framework, Sci Seq, 6, 28517
Park S, Min S, Choi H, Yoon S. deepMiRGene: deep neural network based precursor microrna prediction. arXiv1605.00017.
Lee B, Baek J, Park S, Yoon S. deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks. arXiv1603.09123.
Guigo, 2015, Prescribing splicing, Science, 347, 124, 10.1126/science.aaa4864
Lee T, Yoon S. Boosted categorical restricted boltzmann machine for computational prediction of splice junctions. Proc Int Conf Mach Learn 2015;37.
Lee B, Lee T, Na B, Yoon S. DNA-level splice junction prediction using deep recurrent neural networks. arXiv1512.05135.
Xiong, 2011, Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioinformatics, 27, 2554, 10.1093/bioinformatics/btr444
Leung, 2014, Deep learning of the tissue-regulated splicing code, Bioinformatics, 30, i121, 10.1093/bioinformatics/btu277
Xiong, 2014, The human splicing code reveals new insights into the genetic determinants of disease, Science, 347, 1254806, 10.1126/science.1254806
Quang, 2015, DANN: A deep learning approach for annotating the pathogenicity of genetic variants, Bioinformatics, 31, 761, 10.1093/bioinformatics/btu703
Zhou, 2015, Predicting effects of noncoding variants with deep learning–based sequence model, Nat Methods, 12, 931, 10.1038/nmeth.3547
Quang, 2016, DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences, Nucleic Acids Res, 44, 11, 10.1093/nar/gkw226
Anfinsen, 1972, The formation and stabilization of protein structure, Biochem J, 128, 737, 10.1042/bj1280737
Gibson, 1967, Minimization of polypeptide energy. I. Preliminary structures of bovine pancreatic ribonuclease S-peptide, Proc Natl Acad Sci U S A, 58, 420, 10.1073/pnas.58.2.420
Hammarstrom, 2003, Prevention of transthyretin amyloid disease by changing protein misfolding energetics, Science, 299, 713, 10.1126/science.1079589
Chiti, 2006, Protein misfolding, functional amyloid, and human disease, Annu Rev Biochem, 75, 333, 10.1146/annurev.biochem.75.101304.123901
Selkoe, 2003, Folding proteins in fatal ways, Nature, 426, 900, 10.1038/nature02264
Lyons, 2014, Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network, J Comput Chem, 35, 2040, 10.1002/jcc.23718
Heffernan, 2015, Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning, Sci Rep, 5, 11476, 10.1038/srep11476
Spencer, 2015, A deep learning network approach to ab initio protein secondary structure prediction, IEEE/ACM Trans Comput Biol Bioinform, 12, 103, 10.1109/TCBB.2014.2343960
Baldi, 2000, Matching protein beta-sheet partners by feedforward and recurrent neural networks, Proc Int Conf Intell Syst Mol Biol, 25
Baldi, 1999, Exploiting the past and the future in protein secondary structure prediction, Bioinformatics, 15, 937, 10.1093/bioinformatics/15.11.937
Pollastri, 2002, Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles, Proteins, 47, 228, 10.1002/prot.10082
Pollastri, 2002, Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners, Bioinformatics, 18, 10.1093/bioinformatics/18.suppl_1.S62
Baldi, 2004, The principled design of large-scale recursive neural network architectures-DAG-RNNs and the protein structure prediction problem, J Mach Learn Res, 4, 575
Di Lena, 2012, Deep architectures for protein contact map prediction, Bioinformatics, 28, 2449, 10.1093/bioinformatics/bts475
Sønderby SK, Winther O. Protein secondary structure prediction with long short term memory networks. arXiv1412.7828
Li, 2015, Malphite: a convolutional neural network and ensemble learning based protein secondary structure predictor, Proc IEEE Int Conf Bioinformatics Biomed, 1260
Lin Z, Lanchantin J, Qi Y. MUST-CNN: a multilayer shift-and-stitch deep convolutional architecture for sequence-based protein structure prediction. Proc Conf AAAI Artif Intell 2016:8.
Lena, 2012, Deep spatio-temporal architectures and learning for protein structure prediction, Adv Neural Inf Process Syst, 512
Troyanskaya OG. Deep supervised and convolutional generative stochastic network for protein secondary structure prediction. Proc 31st Int Conf Mach Learn 2014; 32:745–53.
Wang, 2015, DeepCNF-D: predicting protein order/disorder regions by weighted deep convolutional neural fields, Int J Mol Sci, 16, 17315, 10.3390/ijms160817315
Eickholt, 2013, DNdisorder: predicting protein disorder using boosting and deep networks, BMC Bioinformatics, 14, 88, 10.1186/1471-2105-14-88
Wang, 2016, RaptorX-Property: a web server for protein structure property prediction, Nucleic Acids Res, 44, 10.1093/nar/gkw306
Shin, 2011, Autoencoder in time-series analysis for unsupervised tissues characterisation in a large unlabelled medical image dataset, Proc Int Conf Mach Learn Appl, 1, 259
Jia, 2014, A novel semi-supervised deep learning framework for affective state recognition on EEG signals, Proc IEEE Int Symp Bioinformatics Bioeng, 30
He, 2015, Deep residual learning for image recognition, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 7, 171
Szegedy C, Ioffe S, Vanhoucke V. Inception-v4, InceptionResNet and the impact of residual connections on learning. arXiv1602.07261.
Yarlagadda DVK, Rao P, Rao D. MitosisNet: a deep learning network for mitosis detection in breast cancer histopathology images. IEEEEMBS Int Conf BiomedHealthInform 2017.
Irshad H, Oh EY, Schmolze D, Quintana LM, Collins L, Tamimi RM, et al. Crowdsourcing scoring of immunohistochemistry images: evaluating performance of the crowd and an automated computational method. arXiv160606681.
Albarqouni, 2016, AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images, IEEE Trans Med Imaging, 35, 1313, 10.1109/TMI.2016.2528120