DNA sequence classification based on MLP with PILAE algorithm
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[1] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[2] Nung Kion Lee,et al. Evaluation of Convolutionary Neural Networks Modeling of DNA Sequences using Ordinal versus one-hot Encoding Method , 2017 .
[3] Zhi Wei,et al. tRNA-DL: A Deep Learning Approach to Improve tRNAscan-SE Prediction Results , 2019, Human Heredity.
[4] Dongbin Zhao,et al. Pseudoinverse Learners: New Trend and Applications to Big Data , 2019, INNSBDDL.
[5] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Junjie Chen,et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences , 2015, Nucleic Acids Res..
[7] Megan F. Cole,et al. Genome-wide Map of Nucleosome Acetylation and Methylation in Yeast , 2005, Cell.
[8] A. Nandy,et al. Novel techniques of graphical representation and analysis of DNA sequences—A review , 1998, Journal of Biosciences.
[9] Qian Yin,et al. Image Recognition with Histogram of Oriented Gradient Feature and Pseudoinverse Learning AutoEncoders , 2017, ICONIP.
[10] Tony Håndstad,et al. Motif kernel generated by genetic programming improves remote homology and fold detection , 2007, BMC Bioinformatics.
[11] Stéphane Mallat,et al. Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[12] P. Hebert,et al. bold: The Barcode of Life Data System (http://www.barcodinglife.org) , 2007, Molecular ecology notes.
[13] Renfa Li,et al. On the Similarity of DNA Primary Sequences Based on 5-D Representation , 2007 .
[14] Ning Chen,et al. DeepEnhancer: Predicting enhancers by convolutional neural networks , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[15] Klaus Obermayer,et al. Fast model-based protein homology detection without alignment , 2007, Bioinform..
[16] Jiuwen Cao,et al. Protein Sequence Classification with Improved Extreme Learning Machine Algorithms , 2014, BioMed research international.
[17] C. L. Philip Chen,et al. Regularization parameter estimation for feedforward neural networks , 2003 .
[18] Ren Long,et al. iRSpot-EL: identify recombination spots with an ensemble learning approach , 2017, Bioinform..
[19] Yann LeCun,et al. Very Deep Convolutional Networks for Text Classification , 2016, EACL.
[20] Jijun Tang,et al. Prediction of human protein subcellular localization using deep learning , 2017, J. Parallel Distributed Comput..
[21] Ren Long,et al. iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework , 2016, Bioinform..
[22] P. Hebert,et al. bold: The Barcode of Life Data System (http://www.barcodinglife.org) , 2007, Molecular ecology notes.
[23] Alaa Eddin Alchalabi,et al. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks , 2017, Genes.
[24] S. Park,et al. Deep transfer learning approach to predict tumor mutation burden (TMB) and delineate spatial heterogeneity of TMB within tumors from whole slide images , 2019, bioRxiv.
[25] Xiaolong Wang,et al. repRNA: a web server for generating various feature vectors of RNA sequences , 2015, Molecular Genetics and Genomics.
[26] Jianlin Cheng,et al. DNdisorder: predicting protein disorder using boosting and deep networks , 2013, BMC Bioinformatics.
[27] Junjie Chen,et al. Protein remote homology detection based on bidirectional long short-term memory , 2017, BMC Bioinformatics.
[28] P. Hebert,et al. The promise of DNA barcoding for taxonomy. , 2005, Systematic biology.
[29] Kenji Satou,et al. DNA Sequence Classification by Convolutional Neural Network , 2016 .
[30] Tomasz Neugebauer,et al. DNA Data Visualization (DDV): Software for Generating Web-Based Interfaces Supporting Navigation and Analysis of DNA Sequence Data of Entire Genomes , 2015, PloS one.
[31] B. Liu,et al. Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods , 2017, Oncotarget.
[32] David K. Gifford,et al. Convolutional neural network architectures for predicting DNA–protein binding , 2016, Bioinform..
[33] Avanti Shrikumar,et al. Reverse-complement parameter sharing improves deep learning models for genomics , 2017, bioRxiv.
[34] N. Radakovich,et al. Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer , 2019, Journal of pathology informatics.
[35] Antonino Fiannaca,et al. Probabilistic topic modeling for the analysis and classification of genomic sequences , 2015, BMC Bioinformatics.
[36] Atina G. Coté,et al. Evaluation of methods for modeling transcription factor sequence specificity , 2013, Nature Biotechnology.
[37] Antonino Fiannaca,et al. Classification Experiments of DNA Sequences by Using a Deep Neural Network and Chaos Game Representation , 2016, CompSysTech.
[38] Tu Bao Ho,et al. Prediction of Histone Modifications in DNA sequences , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.
[39] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.
[40] Beilun Wang,et al. Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks , 2016, PSB.
[41] Giovanni Felici,et al. Learning to classify species with barcodes , 2009, BMC Bioinformatics.
[42] Michael R. Lyu,et al. A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data , 2004, Neurocomputing.
[43] Antonino Fiannaca,et al. The General Regression Neural Network to Classify Barcode and mini-barcode DNA , 2014, CIBB.
[44] Ke Wang,et al. Autoencoder, low rank approximation and pseudoinverse learning algorithm , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[45] Avanti Shrikumar,et al. Separable Fully Connected Layers Improve Deep Learning Models For Genomics , 2017, bioRxiv.
[46] G. Bejerano,et al. Enhancers: five essential questions , 2013, Nature Reviews Genetics.
[47] Morteza Mohammad Noori,et al. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features , 2014, PLoS Comput. Biol..
[48] Alexander J. Smola,et al. Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.
[49] Ehsaneddin Asgari,et al. ProtVec: A Continuous Distributed Representation of Biological Sequences , 2015, ArXiv.
[50] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[51] W. Wasserman,et al. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods , 2016, BMC Bioinformatics.
[52] Giovanni Felici,et al. Supervised DNA Barcodes species classification: analysis, comparisons and results , 2014, BioData Mining.
[53] Jie Zhang,et al. Reveal the Cognitive Process of Deep Learning during Identifying Nucleosome Occupancy and Histone Modification , 2018, 2018 Chinese Automation Congress (CAC).
[54] Kenji Satou,et al. Application of a Feature Selection Method to Nucleosome Data: Accuracy Improvement and Comparison with Other Methods , 2008 .
[55] D. Bielinska-Waz,et al. Non-standard similarity/dissimilarity analysis of DNA sequences. , 2014, Genomics.
[56] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[57] Aurélien Miralles,et al. The integrative future of taxonomy , 2010, Frontiers in Zoology.
[58] De-Shuang Huang,et al. Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network , 2019, Scientific Reports.
[59] John C. Sanford,et al. Skittle: A 2-Dimensional Genome Visualization Tool , 2009, BMC Bioinformatics.
[60] Sander M. Bohte,et al. An image representation based convolutional network for DNA classification , 2018, ICLR.
[61] Qinghua Hu,et al. HAlign: Fast multiple similar DNA/RNA sequence alignment based on the centre star strategy , 2015, Bioinform..