Deep Learning in Mining Biological Data

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

[1]  Mufti Mahmud,et al.  A Multi-recurrent Network for Crude Oil Price Prediction , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[2]  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.

[3]  Mufti Mahmud,et al.  An event based topic learning pipeline for neuroimaging literature mining , 2020, Brain Informatics.

[4]  H. Chandler.  Database , 1985 .

[5]  Hyoungseop Kim,et al.  GAN-based survival prediction model from CT images of patients with idiopathic pulmonary fibrosis , 2020, Medical Imaging.

[6]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[7]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[8]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[9]  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).

[10]  Bin Yang,et al.  MedGAN: Medical Image Translation using GANs , 2018, Comput. Medical Imaging Graph..

[11]  Michele Giugliano,et al.  A Web-Based Framework for Semi-Online Parallel Processing of Extracellular Neuronal Signals Re- corded by Microelectrode Arrays , 2014 .

[12]  Sungroh Yoon,et al.  Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions , 2015, ICML.

[13]  Navin Kumar Manaswi,et al.  Deep Learning with Applications Using Python , 2018, Apress.

[14]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[15]  Erik Cambria,et al.  Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis , 2018, Cognitive Computation.

[16]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[17]  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).

[18]  Samy Bengio,et al.  The Handbook of Brain Theory and Neural Networks , 2002 .

[19]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[20]  Wolfgang Müller,et al.  Data management and data enrichment for systems biology projects. , 2017, Journal of biotechnology.

[21]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[22]  Gayadhar Pradhan,et al.  A New ECG Denoising Framework Using Generative Adversarial Network , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  Zhen Yang,et al.  EmotionalGAN: Generating ECG to Enhance Emotion State Classification , 2019, Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science.

[24]  Alessandro Sperduti,et al.  Challenges in Deep Learning , 2016, ESANN.

[25]  B. Erickson,et al.  Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[26]  Andrew D. Rouillard,et al.  The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins , 2016, Database J. Biol. Databases Curation.

[27]  Sorin Draghici,et al.  Machine Learning and Its Applications to Biology , 2007, PLoS Comput. Biol..

[28]  G. Cochrane,et al.  The International Nucleotide Sequence Database Collaboration , 2011, Nucleic Acids Res..

[29]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[30]  Amir Hussain,et al.  A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications , 2018, Cognitive Computation.

[31]  Mufti Mahmud,et al.  Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal , 2020, BI.

[32]  Mufti Mahmud,et al.  Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges , 2020, IEEE Access.

[33]  Florence March,et al.  2016 , 2016, Affair of the Heart.

[34]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[35]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[36]  Pierre Machart,et al.  Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks , 2020, Nature Communications.

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

[38]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[39]  Sotiris B. Kotsiantis,et al.  Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.

[40]  K. Latha,et al.  Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition , 2014, Comput. Math. Methods Medicine.

[41]  Naftali Tishby,et al.  Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.

[42]  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..

[43]  Mufti Mahmud,et al.  Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data , 2019, BI.

[44]  Mufti Mahmud,et al.  Effect of Corpora on Classification of Fake News using Naive Bayes Classifier , 2020 .

[45]  Mufti Mahmud,et al.  Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[46]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[47]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[48]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[49]  Mufti Mahmud,et al.  Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective , 2019, BI.

[50]  Lois Magner,et al.  A History of the Life Sciences , 1979 .

[51]  Geoffrey E. Hinton,et al.  On rectified linear units for speech processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[52]  Simone Santini,et al.  The cell-centered database , 2007, Neuroinformatics.

[53]  Samit Bhattacharya,et al.  Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.

[54]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[55]  Ke Yan,et al.  Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks , 2019, Scientific Reports.

[56]  David J. Arenillas,et al.  JASPAR: an open-access database of transcription factor binding profiles , 2018 .

[57]  Xiangqian Ding,et al.  A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks , 2016, Int. J. Comput. Intell. Appl..

[58]  Wojciech Zaremba,et al.  Recurrent Neural Network Regularization , 2014, ArXiv.

[59]  Christof Fetzer,et al.  TensorSCONE: A Secure TensorFlow Framework using Intel SGX , 2019, ArXiv.

[60]  Jian Peng,et al.  Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields , 2015, Scientific Reports.

[61]  Chia-Hua Ho,et al.  Recent Advances of Large-Scale Linear Classification , 2012, Proceedings of the IEEE.

[62]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[63]  F J Gilbert,et al.  Artificial intelligence in breast imaging. , 2019, Clinical radiology.

[64]  Arthur W. Toga,et al.  Construction of a 3D probabilistic atlas of human cortical structures , 2008, NeuroImage.

[65]  Yixue Li,et al.  Big Biological Data: Challenges and Opportunities , 2014, Genom. Proteom. Bioinform..

[66]  R. Vadivambal,et al.  Bio-Imaging: Principles, Techniques, and Applications , 2015 .

[67]  LHAN UY SAL,et al.  An overview of regression techniques for knowledge discovery , 1999 .

[68]  Mufti Mahmud,et al.  fASSERT: A Fuzzy Assistive System for Children with Autism Using Internet of Things , 2018, BI.

[69]  Marco Congedo,et al.  Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a) , 2019 .

[70]  A. Franco,et al.  NeuroImage: Clinical , 2022 .

[71]  Nigam H. Shah,et al.  The Stanford Tissue Microarray Database , 2007, Nucleic Acids Res..

[72]  Marco Congedo,et al.  Brain Invaders Solo versus Collaboration: Multi-User P300-based Brain-Computer Interface Dataset (bi2014b) , 2019 .

[73]  Nathan A. Baker,et al.  Standardizing data , 2008, Nature Cell Biology.

[74]  Klaus-Robert Müller,et al.  Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces , 2011, Neural Computation.

[75]  Heng Huang,et al.  Conditional generative adversarial network for gene expression inference , 2018, Bioinform..

[76]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[77]  Marco Congedo,et al.  Brain Invaders Cooperative versus Competitive: Multi-User P300- based Brain-Computer Interface Dataset (bi2015b) , 2019 .

[78]  Mufti Mahmud,et al.  A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System , 2020, IEEE Access.

[79]  John Quackenbush,et al.  Extracting biology from high-dimensional biological data , 2007, Journal of Experimental Biology.

[80]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[81]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[82]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[83]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[84]  Andreas Holzinger,et al.  Machine Learning for Health Informatics State-of-the-Art and Future Challenges , 2016 .

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

[86]  Mufti Mahmud,et al.  TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique , 2020, Cognitive Computation.

[87]  Talha Iqbal,et al.  Generative Adversarial Network for Medical Images (MI-GAN) , 2018, Journal of Medical Systems.

[88]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[89]  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.

[90]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[91]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[92]  Dinggang Shen,et al.  A Robust Deep Model for Improved Classification of AD/MCI Patients , 2015, IEEE Journal of Biomedical and Health Informatics.

[93]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[94]  Olgert Denas,et al.  Deep modeling of gene expression regulation in an Erythropoiesis model , 2013 .

[95]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[96]  Martin Ester,et al.  Density‐based clustering , 2019, WIREs Data Mining Knowl. Discov..

[97]  Mufti Mahmud,et al.  Machine Learning Based Early Fall Detection for Elderly People with Neurological Disorder Using Multimodal Data Fusion , 2020, BI.

[98]  Jake Bouvrie,et al.  Notes on Convolutional Neural Networks , 2006 .

[99]  Mohammad Havaei,et al.  Deep Learning Trends for Focal Brain Pathology Segmentation in MRI , 2016, Machine Learning for Health Informatics.

[100]  Jürgen Schmidhuber,et al.  Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation , 2015, NIPS.

[101]  Mufti Mahmud,et al.  A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence , 2020, BI.

[102]  Mufti Mahmud,et al.  Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19 , 2020, BI.

[103]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[104]  Thomas Wiatowski,et al.  A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.

[105]  Aldenor G. Santos,et al.  Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles , 2019, Scientific Reports.

[106]  A. D. Gordon.  A Review of Hierarchical Classification , 1987 .

[107]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[108]  Manfredo Atzori,et al.  Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography , 2016 .

[109]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[110]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[111]  Xinghua Lu,et al.  Trans-species learning of cellular signaling systems with bimodal deep belief networks , 2015, Bioinform..

[112]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[113]  Hector Zenil,et al.  An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems , 2019, iScience.

[114]  Chuan Zhou,et al.  Deep Learning in Medical Image Analysis. , 2017, Advances in experimental medicine and biology.

[115]  Hua Xu,et al.  Predict effective drug combination by deep belief network and ontology fingerprints , 2018, J. Biomed. Informatics.

[116]  Ayman El-Baz,et al.  Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network , 2016, ArXiv.

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

[118]  L. Kenny,et al.  ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics , 2011 .

[119]  S. Fong,et al.  Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images , 2020, Cognitive Computation.

[120]  Klaus H. Maier-Hein,et al.  Deep MRI brain extraction: A 3D convolutional neural network for skull stripping , 2016, NeuroImage.

[121]  Seong-Whan Lee,et al.  Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.

[122]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[123]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[124]  A. Rajadhyaksha.  Archive , 2008, BioScope: South Asian Screen Studies.

[125]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[126]  Shuicheng Yan,et al.  Parallel convolutional-linear neural network for motor imagery classification , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

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

[128]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[129]  Yifeng Li,et al.  Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine , 2019, Neurocomputing.

[130]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[131]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[132]  Klaus-Robert Müller,et al.  Introduction to machine learning for brain imaging , 2011, NeuroImage.

[133]  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.

[134]  Liwei Zhang,et al.  Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models , 2018, Methods in molecular biology.

[135]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[136]  William Coleman,et al.  Biology in the Nineteenth Century: Problems of Form, Function and Transformation , 1971 .

[137]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

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

[139]  Baek Hwan Cho,et al.  Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network , 2020, Comput. Methods Programs Biomed..

[140]  Mufti Mahmud,et al.  Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets , 2020, Advances on Smart and Soft Computing.

[141]  Richard N Henson,et al.  A multi-subject, multi-modal human neuroimaging dataset , 2015, Scientific Data.

[142]  Chris Mattmann,et al.  Computing: A vision for data science , 2013, Nature.

[143]  Mufti Mahmud,et al.  Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia , 2020, Brain Informatics.

[144]  Reza Ghaeini,et al.  A Deep Learning Approach for Cancer Detection and Relevant Gene Identification , 2017, PSB.

[145]  S MarcusDaniel,et al.  Open Access Series of Imaging Studies (OASIS) , 2007 .

[146]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[147]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[148]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[149]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[150]  Marco Congedo,et al.  Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a) , 2019 .

[151]  Qiang Wang,et al.  Benchmarking State-of-the-Art Deep Learning Software Tools , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

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

[153]  Hanlee P. Ji,et al.  Next-generation DNA sequencing , 2008, Nature Biotechnology.

[154]  Wolfgang Maass,et al.  Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..

[155]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[156]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[157]  Amir Hussain,et al.  Service Oriented Architecture Based Web Application Model for Collaborative Biomedical Signal Analysis , 2012, Biomedizinische Technik. Biomedical engineering.

[158]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[159]  Pasin Israsena,et al.  EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation , 2014, TheScientificWorldJournal.

[160]  Chin-Teng Lin,et al.  Multi-channel EEG recordings during a sustained-attention driving task , 2018, Scientific Data.

[161]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[162]  Yun Luo,et al.  EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[163]  M. Farah,et al.  Progress and challenges in probing the human brain , 2015, Nature.

[164]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[165]  Byunghan Lee,et al.  Advance Access Publication Date: Day Month Year Manuscript Category Deeptarget: End-to-end Learning Framework for Microrna Target Prediction Using Deep Recurrent Neural Networks , 2022 .

[166]  M. Nicolelis,et al.  Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. , 2017, Physiological reviews.

[167]  Paolo Zaffino,et al.  Deep Neural Networks for Fast Segmentation of 3D Medical Images , 2016, MICCAI.

[168]  Ron Kohavi,et al.  Data mining tasks and methods: Classification: decision-tree discovery , 2002 .

[169]  Bao-Liang Lu,et al.  Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks , 2020, Neurocomputing.

[170]  Seunghyun Park,et al.  deepMiRGene: Deep Neural Network based Precursor microRNA Prediction , 2016, ArXiv.

[171]  Quan Liu,et al.  Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network , 2019, Scientific Reports.

[172]  A. Siepel,et al.  Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data , 2016, Nature Genetics.

[173]  Synho Do,et al.  Medical Image Deep Learning with Hospital PACS Dataset , 2015, ArXiv.

[174]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[175]  David N. Kennedy,et al.  Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Resource Announcement , 2009, Neuroinformatics.

[176]  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).

[177]  Andrey Kan,et al.  Machine learning applications in cell image analysis , 2017, Immunology and cell biology.

[178]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[179]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[180]  David K. Gifford,et al.  Convolutional neural network architectures for predicting DNA–protein binding , 2016, Bioinform..

[181]  Fan Zhao,et al.  Compressing and Accelerating Neural Network for Facial Point Localization , 2018, Cognitive Computation.

[182]  Mufti Mahmud,et al.  Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges , 2016, Front. Neurosci..

[183]  Yuan Zhang,et al.  Affective state recognition from EEG with deep belief networks , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[184]  Yoshua Bengio,et al.  On Training Deep Boltzmann Machines , 2012, ArXiv.

[185]  Mufti Mahmud,et al.  Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder , 2020, BI.

[186]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..

[187]  Anirban Dutta,et al.  Indian genetic disease database , 2010, Nucleic Acids Res..

[188]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[189]  Christian Jutten,et al.  " Brain Invaders": a prototype of an open-source P300-based video game working with the OpenViBE platform , 2011 .

[190]  Nilanjan Dey,et al.  Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images , 2020, Cognitive Computation.

[191]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[192]  Yang Liu,et al.  GANsDTA: Predicting Drug-Target Binding Affinity Using GANs , 2020, Frontiers in Genetics.

[193]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[194]  Mufti Mahmud,et al.  Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources , 2020, BI.

[195]  Alan F. Smeaton,et al.  Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation , 2018, Cognitive Computation.

[196]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[197]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[198]  J.J. Hopfield,et al.  Artificial neural networks , 1988, IEEE Circuits and Devices Magazine.

[199]  H. Altay Güvenir,et al.  An overview of regression techniques for knowledge discovery , 1999, The Knowledge Engineering Review.

[200]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[201]  Xiao Sun,et al.  Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory , 2017, Cognitive Computation.

[202]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[203]  Gustavo Marrero Callicó,et al.  Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology , 2020, Medical Imaging: Digital Pathology.

[204]  Pål Halvorsen,et al.  GANEx: A complete pipeline of training, inference and benchmarking GAN experiments , 2019, 2019 International Conference on Content-Based Multimedia Indexing (CBMI).

[205]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[206]  M. Metzker.  Sequencing technologies — the next generation , 2010, Nature Reviews Genetics.

[207]  Lianghua He,et al.  Classification on ADHD with Deep Learning , 2014, 2014 International Conference on Cloud Computing and Big Data.

[208]  Mufti Mahmud,et al.  One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images , 2020 .

[209]  Xin Bi,et al.  Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine , 2019, Cognitive Computation.

[210]  Murat Kaya,et al.  A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces , 2018, Scientific Data.

[211]  Marc'Aurelio Ranzato,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.

[212]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[213]  Ugur Halici,et al.  A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.

[214]  Emmanuel Abbe,et al.  Provable limitations of deep learning , 2018, ArXiv.

[215]  Mohak Shah,et al.  Comparative Study of Deep Learning Software Frameworks , 2015, 1511.06435.

[216]  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.

[217]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[218]  Amir Hussain,et al.  A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair , 2016, Cognitive Computation.

[219]  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..

[220]  Subramanian Ramanathan,et al.  DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses , 2015, IEEE Transactions on Affective Computing.

[221]  Ahmad Lotfi,et al.  A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living , 2019, Appl. Soft Comput..

[222]  Edmund C. Lalor,et al.  Electrophysiological Correlates of Semantic Dissimilarity Reflect the Comprehension of Natural, Narrative Speech , 2017, Current Biology.

[223]  Mufti Mahmud,et al.  SENSE: a Student Performance Quantifier using Sentiment Analysis , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[224]  Christian Igel,et al.  An Introduction to Restricted Boltzmann Machines , 2012, CIARP.

[225]  Richard O. Sinnott,et al.  Large-scale data sharing in the life sciences: Data standards, incentives, barriers and funding models (The "Joint Data Standards Study") , 2005 .

[226]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[227]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[228]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[229]  Yuchen Fu,et al.  ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient , 2019, IEEE Access.

[230]  Sydney Brenner,et al.  The Revolution in the Life Sciences , 2012, Science.

[231]  Hans-Peter Kriegel,et al.  Density‐based clustering , 2011, WIREs Data Mining Knowl. Discov..

[232]  XUE JIANG,et al.  A Generative Adversarial Network Model for Disease Gene Prediction With RNA-seq Data , 2020, IEEE Access.

[233]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[234]  Navin Kumar Manaswi,et al.  Understanding and Working with Keras , 2018 .

[235]  Zenglin Xu,et al.  TensorD: A tensor decomposition library in TensorFlow , 2018, Neurocomputing.