Applications of Deep Learning and Reinforcement Learning to Biological Data
暂无分享,去创建一个
Amir Hussain | Mufti Mahmud | Stefano Vassanelli | M. S. Kaiser | Mohammed Shamim Kaiser | A. Hussain | M. Mahmud | S. Vassanelli
[1] Sungroh Yoon,et al. Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions , 2015, ICML.
[2] Byunghan Lee,et al. deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks , 2016, BCB.
[3] V. Goh,et al. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks , 2015, PloS one.
[4] Jian Peng,et al. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields , 2015, Scientific Reports.
[5] Paolo Zaffino,et al. Deep Neural Networks for Fast Segmentation of 3D Medical Images , 2016, MICCAI.
[6] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .
[7] Pasin Israsena,et al. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation , 2014, TheScientificWorldJournal.
[8] Klaus-Robert Müller,et al. Interpretable deep neural networks for single-trial EEG classification , 2016, Journal of Neuroscience Methods.
[9] Brendan J. Frey,et al. Classifying and segmenting microscopy images with deep multiple instance learning , 2015, Bioinform..
[10] Aidong Zhang,et al. A Novel Semi-Supervised Deep Learning Framework for Affective State Recognition on EEG Signals , 2014, 2014 IEEE International Conference on Bioinformatics and Bioengineering.
[11] Alireza Gharabaghi,et al. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation , 2015, Front. Neurosci..
[12] Jianyang Zeng,et al. Deep learning with feature embedding for compound-protein interaction prediction , 2016, bioRxiv.
[13] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[14] Christian Igel,et al. An Introduction to Restricted Boltzmann Machines , 2012, CIARP.
[15] Djemel Ziou,et al. Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.
[16] Brendan J. Frey,et al. A compendium of RNA-binding motifs for decoding gene regulation , 2013, Nature.
[17] Shalabh Bhatnagar,et al. Fast gradient-descent methods for temporal-difference learning with linear function approximation , 2009, ICML '09.
[18] Alex Bateman,et al. An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.
[19] Yixue Li,et al. Big Biological Data: Challenges and Opportunities , 2014, Genom. Proteom. Bioinform..
[20] 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.
[21] R. Vadivambal,et al. Bio-Imaging: Principles, Techniques, and Applications , 2015 .
[22] Potter Wickware. Next-generation biologists must straddle computation and biology , 2000, Nature.
[23] Haitao Wang,et al. Deep reinforcement learning with experience replay based on SARSA , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).
[24] Hado van Hasselt,et al. Double Q-learning , 2010, NIPS.
[25] A. J. Meadows,et al. A revolution in life sciences , 1992 .
[26] Marius George Linguraru,et al. Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation , 2016, IEEE Transactions on Medical Imaging.
[27] Xiaofang Zhang,et al. Protein-Protein Interaction Network Constructing Based on Text Mining and Reinforcement Learning with Application to Prostate Cancer , 2014, 2015 IEEE Trustcom/BigDataSE/ISPA.
[28] Jin Chen,et al. A hybrid convolutional neural networks with extreme learning machine for WCE image classification , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[29] Lovedeep Gondara,et al. Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[30] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[31] O. Stegle,et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2017, Genome Biology.
[32] Hyun-Soo Choi,et al. FingerNet: Deep learning-based robust finger joint detection from radiographs , 2015, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS).
[33] Shuicheng Yan,et al. Parallel convolutional-linear neural network for motor imagery classification , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).
[34] William Stafford Noble,et al. Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.
[35] Leopold Parts,et al. Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning , 2016, G3: Genes, Genomes, Genetics.
[36] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[37] Amir Hussain,et al. Service Oriented Architecture Based Web Application Model for Collaborative Biomedical Signal Analysis , 2012, Biomedizinische Technik. Biomedical engineering.
[38] Xiaohong W. Gao,et al. Classification of CT brain images based on deep learning networks , 2017, Comput. Methods Programs Biomed..
[39] J. Stuart-Glennie,et al. History as a Science , 1901, Nature.
[40] Yi Shi,et al. DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations , 2016, BMC Bioinformatics.
[41] Xinghua Lu,et al. Trans-species learning of cellular signaling systems with bimodal deep belief networks , 2015, Bioinform..
[42] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[43] Jianyang Zeng,et al. A deep learning framework for modeling structural features of RNA-binding protein targets , 2015, Nucleic acids research.
[44] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[45] Geoffrey H. Ball,et al. ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .
[46] Jürgen Schmidhuber,et al. Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation , 2015, NIPS.
[47] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] 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.
[50] Guang-Zhong Yang,et al. Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.
[51] Honglak Lee,et al. Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising , 2013, NIPS.
[52] Andrzej Cichocki,et al. Deep Learning of Multifractal Attributes from Motor Imagery Induced EEG , 2014, ICONIP.
[53] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[54] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[55] Byunghan Lee,et al. Deep learning in bioinformatics , 2016, Briefings Bioinform..
[56] Nando de Freitas,et al. Sample Efficient Actor-Critic with Experience Replay , 2016, ICLR.
[57] David Heckerman,et al. A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.
[58] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[59] Romain Hérault,et al. IODA: An input/output deep architecture for image labeling , 2015, Pattern Recognit..
[60] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[61] Tom Schaul,et al. Prioritized Experience Replay , 2015, ICLR.
[62] Sergey Levine,et al. Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.
[63] Synho Do,et al. Medical Image Deep Learning with Hospital PACS Dataset , 2015, ArXiv.
[64] Yann LeCun,et al. Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.
[65] Olgert Denas,et al. Deep modeling of gene expression regulation in an Erythropoiesis model , 2013 .
[66] Xiangqian Ding,et al. A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks , 2016, Int. J. Comput. Intell. Appl..
[67] M. Nicolelis,et al. Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. , 2017, Physiological reviews.
[68] Ugur Halici,et al. A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.
[69] Dinggang Shen,et al. Machine Learning in Medical Imaging , 2012, Lecture Notes in Computer Science.
[70] Miguel Ángel Guevara-López,et al. Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..
[71] Dinggang Shen,et al. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients , 2016, MICCAI.
[72] Maria-Iuliana Bocicor,et al. A Reinforcement Learning Approach for Solving the Fragment Assembly Problem , 2011, 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.
[73] Jundong Liu,et al. Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis , 2017, Pattern Recognit..
[74] Mohammad Havaei,et al. Deep Learning Trends for Focal Brain Pathology Segmentation in MRI , 2016, Machine Learning for Health Informatics.
[75] Michele Giugliano,et al. QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays , 2014, Front. Neuroinform..
[76] Fang Wang,et al. A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning , 2013 .
[77] Fabian J. Theis,et al. Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells , 2016 .
[78] José Carlos Príncipe,et al. Coadaptive Brain–Machine Interface via Reinforcement Learning , 2009, IEEE Transactions on Biomedical Engineering.
[79] Ata Mahjoubfar,et al. Deep Learning in Label-free Cell Classification , 2016, Scientific Reports.
[80] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[81] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[82] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[83] Kai Xu,et al. Quantized Attention-Gated Kernel Reinforcement Learning for Brain–Machine Interface Decoding , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[84] Yan Xu,et al. Deep learning of feature representation with multiple instance learning for medical image analysis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[85] Richard D. Jones,et al. EEG-Based Lapse Detection With High Temporal Resolution , 2007, IEEE Transactions on Biomedical Engineering.
[86] WangYing,et al. A deep feature based framework for breast masses classification , 2016 .
[87] Xiao Zhang,et al. Convolutional Neural Networks in Automatic Recognition of Trans-differentiated Neural Progenitor Cells under Bright-Field Microscopy , 2015, 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC).
[88] L Poole David,et al. Artificial Intelligence: Foundations of Computational Agents , 2010 .
[89] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[90] Atina G. Coté,et al. Evaluation of methods for modeling transcription factor sequence specificity , 2013, Nature Biotechnology.
[91] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[92] Pierre Baldi,et al. Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.
[93] Tonio Ball,et al. A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping , 2014, IUI.
[94] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[95] Ayman El-Baz,et al. Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network , 2016, ArXiv.
[96] Justin A. Blanco,et al. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.
[97] A. Siepel,et al. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data , 2016, Nature Genetics.
[98] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[99] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.
[100] Klaus H. Maier-Hein,et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping , 2016, NeuroImage.
[101] Yuan Zhang,et al. Affective state recognition from EEG with deep belief networks , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.
[102] Andrey Kan,et al. Machine learning applications in cell image analysis , 2017, Immunology and cell biology.
[103] J. C. Sanchez,et al. Control of a center-out reaching task using a reinforcement learning Brain-Machine Interface , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.
[104] William B. Claster,et al. Deep Learning with Convolutional Neural Networks , 2020, Mathematics and Programming for Machine Learning with R.
[105] Na Lu,et al. A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[106] Lei Wang,et al. A restricted Boltzmann machine based two-lead electrocardiography classification , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
[107] James G. Lyons,et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning , 2015, Scientific Reports.
[108] Yuxi Li,et al. Deep Reinforcement Learning: An Overview , 2017, ArXiv.
[109] Chia-Hua Ho,et al. Recent Advances of Large-Scale Linear Classification , 2012, Proceedings of the IEEE.
[110] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[111] Lianghua He,et al. A Deep Learning Method for Classification of EEG Data Based on Motor Imagery , 2014, ICIC.
[112] Gustavo Carneiro,et al. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance , 2017, Medical Image Anal..
[113] Seunghyun Park,et al. deepMiRGene: Deep Neural Network based Precursor microRNA Prediction , 2016, ArXiv.
[114] M. Farah,et al. Progress and challenges in probing the human brain , 2015, Nature.
[115] Hanlee P. Ji,et al. Next-generation DNA sequencing , 2008, Nature Biotechnology.
[116] Reza Ghaeini,et al. A Deep Learning Approach for Cancer Detection and Relevant Gene Identification , 2017, PSB.
[117] Justin C. Sanchez,et al. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning , 2013, Journal of neural engineering.
[118] Alberto Signoroni,et al. Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging , 2017, Pattern Recognit..
[119] Abhijit Gosavi,et al. Reinforcement Learning: A Tutorial Survey and Recent Advances , 2009, INFORMS J. Comput..
[120] Manfredo Atzori,et al. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands , 2016, Front. Neurorobot..
[121] Samit Bhattacharya,et al. Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.
[122] Peter Stone,et al. Reinforcement learning , 2019, Scholarpedia.
[123] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[124] Fuad E. Alsaadi,et al. Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip , 2016, Cognitive Computation.
[125] Donald C. Wunsch,et al. Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG , 2000, Neurocomputing.
[126] M. Metzker. Sequencing technologies — the next generation , 2010, Nature Reviews Genetics.
[127] Yi Li,et al. Gene expression inference with deep learning , 2015, bioRxiv.
[128] Jun Ye,et al. CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning , 2017, Molecular informatics.
[129] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[130] David K. Gifford,et al. Convolutional neural network architectures for predicting DNA–protein binding , 2016, Bioinform..
[131] Andrew G. Barto,et al. Reinforcement learning , 1998 .
[132] Seong-Whan Lee,et al. Movement intention decoding based on deep learning for multiuser myoelectric interfaces , 2016, 2016 4th International Winter Conference on Brain-Computer Interface (BCI).
[133] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[134] Michele Giugliano,et al. A Web-Based Framework for Semi-Online Parallel Processing of Extracellular Neuronal Signals Re- corded by Microelectrode Arrays , 2014 .
[135] Vince D. Calhoun,et al. Deep learning for neuroimaging: a validation study , 2013, Front. Neurosci..
[136] David Silver,et al. Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.
[137] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[138] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[139] Rasool Fakoor,et al. Using deep learning to enhance cancer diagnosis and classication , 2013 .
[140] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[141] Mohammed Yeasin,et al. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks , 2015, ICLR.
[142] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[143] J H Young,et al. History of life sciences. , 1974, Science.
[144] Jianzhong Wu,et al. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.
[145] Yoshua Bengio,et al. On Training Deep Boltzmann Machines , 2012, ArXiv.
[146] Alessandro Sperduti,et al. Challenges in Deep Learning , 2016, ESANN.
[147] Xinbo Gao,et al. A deep feature based framework for breast masses classification , 2016, Neurocomputing.
[148] Amy Loutfi,et al. Sleep Stage Classification Using Unsupervised Feature Learning , 2012, Adv. Artif. Neural Syst..
[149] Luca Maria Gambardella,et al. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.
[150] Dinggang Shen,et al. Deep Learning-Based Feature Representation for AD/MCI Classification , 2013, MICCAI.
[151] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[152] Vincent Kanade,et al. Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.
[153] Tatsuhiko Tsunoda,et al. A Deep Learning Approach for Motor Imagery EEG Signal Classification , 2016, 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).
[154] Dinggang Shen,et al. A Robust Deep Model for Improved Classification of AD/MCI Patients , 2015, IEEE Journal of Biomedical and Health Informatics.
[155] Luca Maria Gambardella,et al. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.
[156] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[157] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[158] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[159] Naif Alajlan,et al. Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..
[160] Ron Kohavi,et al. Data mining tasks and methods: Classification: decision-tree discovery , 2002 .
[161] Nico Karssemeijer,et al. Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..
[162] Marios Anthimopoulos,et al. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[163] Shuigeng Zhou,et al. Boosting compound-protein interaction prediction by deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[164] Bradley J. Erickson,et al. Toolkits and Libraries for Deep Learning , 2017, Journal of Digital Imaging.
[165] Seong-Whan Lee,et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.
[166] Ghassem Tofighi,et al. DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI , 2016, bioRxiv.
[167] Yue Zhang,et al. Classification of Electrocardiogram Signals with Deep Belief Networks , 2014, CSE.
[168] Yufei Huang,et al. Prediction of driver's drowsy and alert states from EEG signals with deep learning , 2015, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).
[169] Tom Schaul,et al. Universal Value Function Approximators , 2015, ICML.
[170] Gustavo Carneiro,et al. A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..
[171] Justin C. Sanchez,et al. A Symbiotic Brain-Machine Interface through Value-Based Decision Making , 2011, PloS one.
[172] Mufti Mahmud,et al. Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges , 2016, Front. Neurosci..
[173] Célia Ghedini Ralha,et al. Reinforcement Learning Method for BioAgents , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.
[174] Sorin Draghici,et al. Machine Learning and Its Applications to Biology , 2007, PLoS Comput. Biol..
[175] Hamid R. Tizhoosh,et al. Application of reinforcement learning for segmentation of transrectal ultrasound images , 2008, BMC Medical Imaging.
[176] Justin C. Sanchez,et al. Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization , 2014, PloS one.
[177] Xiaohui Xie,et al. DANN: a deep learning approach for annotating the pathogenicity of genetic variants , 2015, Bioinform..
[178] Diogo M. Camacho,et al. Wisdom of crowds for robust gene network inference , 2012, Nature Methods.
[179] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[180] Li-Yeh Chuang,et al. Operon Prediction Using Particle Swarm Optimization and Reinforcement Learning , 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.
[181] Wei Shen,et al. Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification , 2017, Pattern Recognit..
[182] Mohammad Soleymani,et al. Continuous emotion detection using EEG signals and facial expressions , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).
[183] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[184] Mahesan Niranjan,et al. On-line Q-learning using connectionist systems , 1994 .
[185] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[186] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[187] A. Roli. Artificial Neural Networks , 2012, Lecture Notes in Computer Science.
[188] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[189] M. Avendi,et al. Fully automatic segmentation of heart chambers in cardiac MRI using deep learning , 2016, Journal of Cardiovascular Magnetic Resonance.
[190] Jianlin Cheng,et al. A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning , 2016, Journal of proteomics & bioinformatics.
[191] Wenqing Sun,et al. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data , 2017, Comput. Medical Imaging Graph..
[192] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[193] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[194] Alex Zhavoronkov,et al. Applications of Deep Learning in Biomedicine. , 2016, Molecular pharmaceutics.
[195] Lianghua He,et al. Classification on ADHD with Deep Learning , 2014, 2014 International Conference on Cloud Computing and Big Data.
[196] Hao Chen,et al. Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.
[197] Mohamed A. Ismail,et al. Multi-level gene/MiRNA feature selection using deep belief nets and active learning , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[198] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[199] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[200] Ronald M. Summers,et al. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.
[201] Aaron C. Courville,et al. Understanding Representations Learned in Deep Architectures , 2010 .
[202] Ganesh R. Naik,et al. Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks , 2017, Front. Neurosci..
[203] Hao Chen,et al. 3D deeply supervised network for automated segmentation of volumetric medical images , 2017, Medical Image Anal..
[204] Bart De Schutter,et al. Reinforcement Learning and Dynamic Programming Using Function Approximators , 2010 .
[205] Zhaoxiang Zhang,et al. Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition , 2017, Cognitive Computation.
[206] Zachary Chase Lipton. A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.
[207] Lianghua He,et al. Deep Learning in the EEG Diagnosis of Alzheimer's Disease , 2014, ACCV Workshops.
[208] Nima Tajbakhsh,et al. Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs , 2017, Pattern Recognit..
[209] William Coleman,et al. Biology in the Nineteenth Century: Problems of Form, Function and Transformation , 1971 .
[210] Pierre Yves Glorennec,et al. Reinforcement Learning: an Overview , 2000 .
[211] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[212] Yann LeCun,et al. Toward automatic phenotyping of developing embryos from videos , 2005, IEEE Transactions on Image Processing.
[213] Thomas Wiatowski,et al. A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.
[214] Roger C. Tam,et al. Manifold Learning of Brain MRIs by Deep Learning , 2013, MICCAI.
[215] Cuntai Guan,et al. On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[216] Hao Chen,et al. Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..
[217] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[218] Shihui Ying,et al. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease , 2018, IEEE Journal of Biomedical and Health Informatics.
[219] Razvan Pascanu,et al. How to Construct Deep Recurrent Neural Networks , 2013, ICLR.
[220] Bao-Liang Lu,et al. Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks , 2015, IEEE Transactions on Autonomous Mental Development.