Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network

[1]  De-Shuang Huang,et al.  Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  De-Shuang Huang,et al.  High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[3]  Kenli Li,et al.  COPCOP: A Novel Algorithm and Parallel Optimization Framework for Co-Evolutionary Domain Detection. , 2018, IEEE/ACM transactions on computational biology and bioinformatics.

[4]  Zhen Gao,et al.  Computational modeling of in vivo and in vitro protein‐DNA interactions by multiple instance learning , 2017, Bioinform..

[5]  Avanti Shrikumar,et al.  Reverse-complement parameter sharing improves deep learning models for genomics , 2017, bioRxiv.

[6]  May D. Wang,et al.  DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins , 2016, bioRxiv.

[7]  Mattia Antonino Di Gangi,et al.  Deep Learning Architectures for DNA Sequence Classification , 2016, WILF.

[8]  Morteza Mohammad Noori,et al.  gkmSVM: an R package for gapped-kmer SVM , 2016, Bioinform..

[9]  Matthieu Cord,et al.  WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[11]  Johannes Söding,et al.  Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences , 2016, bioRxiv.

[12]  Brendan J. Frey,et al.  Classifying and segmenting microscopy images with deep multiple instance learning , 2015, Bioinform..

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

[14]  De-Shuang Huang,et al.  ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  De-Shuang Huang,et al.  Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Xiaohui S. Xie,et al.  DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.

[17]  Ivo Grosse,et al.  Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data , 2015, BMC Bioinformatics.

[18]  Jens Keilwagen,et al.  Varying levels of complexity in transcription factor binding motifs , 2015, Nucleic acids research.

[19]  Cory B. Giles,et al.  Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data , 2015, BMC Bioinformatics.

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

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

[22]  De-Shuang Huang,et al.  A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks , 2015, IEEE Transactions on NanoBioscience.

[23]  R. Mann,et al.  Quantitative modeling of transcription factor binding specificities using DNA shape , 2015, Proceedings of the National Academy of Sciences.

[24]  De-Shuang Huang,et al.  Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks , 2015, BMC Genomics.

[25]  Lei Zhang,et al.  Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. , 2014, Current protein & peptide science.

[26]  R. Shamir,et al.  A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data , 2014, Nucleic acids research.

[27]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[28]  Zhu-Hong You,et al.  t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks , 2013, PloS one.

[29]  De-Shuang Huang,et al.  Normalized Feature Vectors: A Novel Alignment-Free Sequence Comparison Method Based on the Numbers of Adjacent Amino Acids , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[30]  Atina G. Coté,et al.  Evaluation of methods for modeling transcription factor sequence specificity , 2013, Nature Biotechnology.

[31]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[32]  T. Furey ChIP – seq and beyond : new and improved methodologies to detect and characterize protein – DNA interactions , 2012 .

[33]  De-Shuang Huang,et al.  A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  Simon C. K. Shiu,et al.  Molecular Pattern Discovery Based on Penalized Matrix Decomposition , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[35]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[36]  H. Lähdesmäki,et al.  A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays , 2011, PloS one.

[37]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[38]  Lei Zhang,et al.  Tumor Clustering Using Nonnegative Matrix Factorization With Gene Selection , 2009, IEEE Transactions on Information Technology in Biomedicine.

[39]  Daniel E. Newburger,et al.  Diversity and Complexity in DNA Recognition by Transcription Factors , 2009, Science.

[40]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[41]  Raja Jothi,et al.  Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data , 2008, Nucleic acids research.

[42]  Steven J. M. Jones,et al.  Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques. , 2006, Genome research.

[43]  De-Shuang Huang,et al.  Independent component analysis-based penalized discriminant method for tumor classification using gene expression data , 2006, Bioinform..

[44]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[45]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[46]  Terence P. Speed,et al.  Finding short DNA motifs using permuted markov models , 2004, RECOMB.

[47]  Gary D. Stormo,et al.  DNA binding sites: representation and discovery , 2000, Bioinform..

[48]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

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

[50]  G. Stormo Consensus patterns in DNA. , 1990, Methods in enzymology.