Deep Neural Network for Analysis of DNA Methylation Data
暂无分享,去创建一个
[1] Jun Guo,et al. Cross-modal subspace learning for sketch-based image retrieval: A comparative study , 2016, 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC).
[2] N. Nasios,et al. Variational learning for Gaussian mixture models , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[3] Nizar Bouguila,et al. Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation , 2013, Neural Computing and Applications.
[4] Honglak Lee,et al. Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines , 2013, ICML.
[5] Andrew R. Jamieson,et al. Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE. , 2009, Medical physics.
[6] José M. Bioucas-Dias,et al. Hyperspectral Unmixing Based on Mixtures of Dirichlet Components , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[7] Olivier Thonnard,et al. An Experimental Study of Diversity with Off-the-Shelf AntiVirus Engines , 2009, 2009 Eighth IEEE International Symposium on Network Computing and Applications.
[8] Jun Guo,et al. Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[9] Honggang Zhang,et al. Variational Bayesian Matrix Factorization for Bounded Support Data , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] B. Everitt,et al. Finite Mixture Distributions , 1981 .
[11] Xiao Zhang,et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis , 2010, BMC Bioinformatics.
[12] Markus Flierl,et al. Probabilistic Multiview Depth Image Enhancement Using Variational Inference , 2015, IEEE Journal of Selected Topics in Signal Processing.
[13] Zhanyu Ma. Bayesian estimation of the Dirichlet distribution with expectation propagation , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).
[14] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine-mediated learning.
[15] P. Green,et al. Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .
[16] Pasin Israsena,et al. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation , 2014, TheScientificWorldJournal.
[17] Guoli Wang,et al. LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates , 2006, BMC Bioinformatics.
[18] Nizar Bouguila,et al. Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach , 2006, IEEE Transactions on Knowledge and Data Engineering.
[19] A. Bird,et al. CpG islands and the regulation of transcription. , 2011, Genes & development.
[20] Qie Sun,et al. Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems , 2014 .
[21] Weichung Joe Shih,et al. A mixture model for estimating the local false discovery rate in DNA microarray analysis , 2004, Bioinform..
[22] Alfred O. Hero,et al. A Survey of Stochastic Simulation and Optimization Methods in Signal Processing , 2015, IEEE Journal of Selected Topics in Signal Processing.
[23] Jaehoon Jung,et al. Capacity and Error Probability Analysis of Diversity Reception Schemes Over Generalized- $K$ Fading Channels Using a Mixture Gamma Distribution , 2014, IEEE Transactions on Wireless Communications.
[24] Arne Leijon,et al. Human audio-visual consonant recognition analyzed with three bimodal integration models , 2009, INTERSPEECH.
[25] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[26] Jun Guo,et al. Dirichlet mixture modeling to estimate an empirical lower bound for LSF quantization , 2014, Signal Process..
[27] Jun Guo,et al. Line spectral frequencies modeling by a mixture of von Mises-Fisher distributions , 2015, Signal Process..
[28] Lei Huang,et al. Bayesian Information Criterion for Source Enumeration in Large-Scale Adaptive Antenna Array , 2016, IEEE Transactions on Vehicular Technology.
[29] Arne Leijon,et al. Vector quantization of LSF parameters with a mixture of dirichlet distributions , 2013, IEEE Transactions on Audio, Speech, and Language Processing.
[30] Brendan J. Frey,et al. Deep learning of the tissue-regulated splicing code , 2014, Bioinform..
[31] Qi He,et al. Keep It Simple with Time: A Reexamination of Probabilistic Topic Detection Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Maya R. Gupta,et al. Introduction to the Dirichlet Distribution and Related Processes , 2010 .
[33] Marina Meila,et al. Bayesian Non-Parametric Clustering of Ranking Data , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Hagai Attias,et al. A Variational Bayesian Framework for Graphical Models , 1999 .
[35] Jun Guo,et al. DNN Filter Bank Cepstral Coefficients for Spoofing Detection , 2017, IEEE Access.
[36] Tyson A. Clark,et al. Direct detection of DNA methylation during single-molecule, real-time sequencing , 2010, Nature Methods.
[37] Peter W. Laird,et al. A comparison of cluster analysis methods using DNA methylation data , 2004, Bioinform..
[38] Lawrence Carin,et al. Hidden Markov Models With Stick-Breaking Priors , 2009, IEEE Transactions on Signal Processing.
[39] Arne Leijon,et al. Human skin color detection in RGB space with Bayesian estimation of beta mixture models , 2010, 2010 18th European Signal Processing Conference.
[40] Douglas Eck,et al. Learning Features from Music Audio with Deep Belief Networks , 2010, ISMIR.
[41] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[42] Sotirios Chatzis,et al. The infinite Hidden Markov random field model , 2009, ICCV.
[43] Jun Guo,et al. Cross-modal subspace learning for fine-grained sketch-based image retrieval , 2017, Neurocomputing.
[44] M. Esteller,et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome , 2011, Epigenetics.
[45] Nizar Bouguila,et al. Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application , 2004, IEEE Transactions on Image Processing.
[46] Nizar Bouguila,et al. A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling , 2010, IEEE Transactions on Neural Networks.
[47] Zhengrong Liang,et al. An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis , 2009, IEEE Transactions on Medical Imaging.
[48] Nizar Bouguila,et al. Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification , 2012, IEEE Transactions on Knowledge and Data Engineering.
[49] Jun Guo,et al. Activation force-based air pollution tracing , 2016, 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC).
[50] C. Antoniak. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .
[51] 小森和樹. Gene Expression Omnibus利用方法の検討 , 2016 .
[52] Jun Guo,et al. Feature selection for neutral vector in EEG signal classification , 2016, Neurocomputing.
[53] Honggang Zhang,et al. Cycled merging registration of point clouds for 3D human body modeling , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).
[54] Jun Guo,et al. The Role of Data Analysis in the Development of Intelligent Energy Networks , 2017, IEEE Network.
[55] Jun S. Liu,et al. The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .
[56] Jun Guo,et al. Effect of multi-condition training and speech enhancement methods on spoofing detection , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).
[57] Markus Flierl,et al. Bayesian estimation of Dirichlet mixture model with variational inference , 2014, Pattern Recognit..
[58] Christian P. Robert,et al. The Bayesian choice : from decision-theoretic foundations to computational implementation , 2007 .
[59] Jalil Taghia,et al. Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis , 2014, International journal of molecular sciences.
[60] Richard Walker,et al. PD Disease State Assessment in Naturalistic Environments Using Deep Learning , 2015, AAAI.
[61] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[62] Fredrik Wallin,et al. A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks , 2016, IEEE Internet of Things Journal.
[63] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[64] Honggang Zhang,et al. Nonlinear estimation of missing ΔLSF parameters by a mixture of Dirichlet distributions , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[65] Arne Leijon,et al. Predictive Distribution of the Dirichlet Mixture Model by Local Variational Inference , 2014, J. Signal Process. Syst..
[66] Zhanyu Ma,et al. A probabilistic principal component analysis based hidden Markov model for audio-visual speech recognition , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.
[67] Douglas A. Reynolds,et al. Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..
[68] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[69] Izhar Wallach,et al. The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding , 2009, Bioinform..
[70] Christopher Holmes,et al. Bayesian Nonparametrics: Frontmatter , 2010 .
[71] Arne Leijon,et al. Modelling speech line spectral frequencies with dirichlet mixture models , 2010, INTERSPEECH.
[72] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[73] Chong Wang,et al. Nested Hierarchical Dirichlet Processes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[74] Jianhua Zhang,et al. Data scheme-based wireless channel modeling method: motivation, principle and performance , 2017, Journal of Communications and Information Networks.
[75] Nizar Bouguila,et al. High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[76] Jalil Taghia,et al. Variational Inference for Watson Mixture Model , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[77] Zhongwei Si,et al. Learning Deep Features for DNA Methylation Data Analysis , 2016, IEEE Access.
[78] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[79] Nicholas J. Foti,et al. A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[80] W. Gilks. Markov Chain Monte Carlo , 2005 .
[81] Zhen Yang,et al. The infinite Student's t-factor mixture analyzer for robust clustering and classification , 2012, Pattern Recognit..
[82] Nizar Bouguila,et al. Visual Scenes Categorization Using a Flexible Hierarchical Mixture Model Supporting Users Ontology , 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.
[83] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[84] Nizar Bouguila,et al. Topic Novelty Detection Using Infinite Variational Inverted Dirichlet Mixture Models , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
[85] Jun Liu,et al. User intention understanding from scratch , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).
[86] Zhanyu Ma,et al. A variational Bayes beta Mixture Model for Feature Selection in DNA methylation Studies , 2013, J. Bioinform. Comput. Biol..
[87] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[88] Nizar Bouguila,et al. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[89] Carsten Wiuf,et al. A Beta-mixture model for dimensionality reduction, sample classification and analysis , 2011, BMC Bioinformatics.
[90] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[91] Masakazu Matsugu,et al. Subject independent facial expression recognition with robust face detection using a convolutional neural network , 2003, Neural Networks.
[92] Zhu Han,et al. Unsupervised Profiling of Microglial Arbor Morphologies and Distribution Using a Nonparametric Bayesian Approach , 2016, IEEE Journal of Selected Topics in Signal Processing.
[93] Margaret R. Karagas,et al. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions , 2008, BMC Bioinformatics.
[94] Jalil Taghia,et al. Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[95] Stefanos Zafeiriou,et al. Variational Infinite Hidden Conditional Random Fields , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[96] Arturas Petronis,et al. Epigenetics as a unifying principle in the aetiology of complex traits and diseases , 2010, Nature.
[97] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[98] Markus Flierl,et al. Multiview depth map enhancement by variational bayes inference estimation of Dirichlet mixture models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[99] Xiaoming Chen. Using Akaike Information Criterion for Selecting the Field Distribution in a Reverberation Chamber , 2013, IEEE Transactions on Electromagnetic Compatibility.
[100] David J. Miller,et al. Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures , 2010, IEEE Journal of Selected Topics in Signal Processing.
[101] Arne Leijon,et al. Expectation propagation for estimating the parameters of the beta distribution , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[102] Jun Guo,et al. Histogram transform model using MFCC features for text-independent speaker identification , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.
[103] Nizar Bouguila,et al. Positive vectors clustering using inverted Dirichlet finite mixture models , 2012, Expert Syst. Appl..
[104] Arne Leijon,et al. Bayesian Estimation of Beta Mixture Models with Variational Inference , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[105] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[106] Chunguang Li,et al. The infinite Student's t-mixture for robust modeling , 2012, Signal Process..
[107] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[108] Arne Leijon,et al. PDF-optimized LSF vector quantization based on beta mixture models , 2010, INTERSPEECH.
[109] H. Kitchener,et al. The Dynamics and Prognostic Potential of DNA Methylation Changes at Stem Cell Gene Loci in Women's Cancer , 2012, PLoS genetics.
[110] R. A. Leibler,et al. On Information and Sufficiency , 1951 .