Structural Learning of Proteins Using Graph Convolutional Neural Networks
暂无分享,去创建一个
[1] Jacob D. Durrant,et al. NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes , 2010, J. Chem. Inf. Model..
[2] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Carla Mattos,et al. The K-Ras, N-Ras, and H-Ras Isoforms: Unique Conformational Preferences and Implications for Targeting Oncogenic Mutants. , 2018, Cold Spring Harbor perspectives in medicine.
[4] Gianni De Fabritiis,et al. DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks , 2017, Bioinform..
[5] Teruki Honma,et al. Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening , 2010, J. Chem. Inf. Model..
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Pierre Baldi,et al. Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matching , 2005, Proteins.
[8] Izhar Wallach,et al. AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery , 2015, ArXiv.
[9] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] Rasiah Loganantharaj,et al. A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure , 2017, bioRxiv.
[11] Dong Xu,et al. A sampling-based method for ranking protein structural models by integrating multiple scores and features. , 2011, Current protein & peptide science.
[12] Jun Li,et al. RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks , 2018, PLoS Comput. Biol..
[13] Jason Weston,et al. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition , 2007, BMC Bioinformatics.
[14] Pierre Baldi,et al. A machine learning information retrieval approach to protein fold recognition. , 2006, Bioinformatics.
[15] Silvia Crivelli,et al. A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification , 2018, ArXiv.
[16] Carla Mattos,et al. The small GTPases K-Ras, N-Ras, and H-Ras have distinct biochemical properties determined by allosteric effects , 2017, The Journal of Biological Chemistry.
[17] Yang Zhang,et al. SPICKER: A clustering approach to identify near‐native protein folds , 2004, J. Comput. Chem..
[18] John B. O. Mitchell,et al. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking , 2010, Bioinform..
[19] Jacob D. Durrant,et al. NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function , 2011, J. Chem. Inf. Model..
[20] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[21] Nathan D. Cahill,et al. Robust Spatial Filtering With Graph Convolutional Neural Networks , 2017, IEEE Journal of Selected Topics in Signal Processing.
[22] Chen Keasar,et al. Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[23] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] David Ryan Koes,et al. Protein-Ligand Scoring with Convolutional Neural Networks , 2016, Journal of chemical information and modeling.
[25] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Balachandran Manavalan,et al. Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms , 2014, PloS one.
[27] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[28] Liam J. McGuffin,et al. The ModFOLD server for the quality assessment of protein structural models , 2008, Bioinform..
[29] Björn Wallner,et al. Improved model quality assessment using ProQ2 , 2012, BMC Bioinformatics.
[30] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[31] Q. Zou,et al. Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition , 2016, International journal of molecular sciences.
[32] Jianlin Cheng,et al. Evaluating the absolute quality of a single protein model using structural features and support vector machines , 2009, Proteins.
[33] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] A. Gorin,et al. Protein docking using surface matching and supervised machine learning , 2007, Proteins.
[35] Cengiz Öztireli,et al. Towards better understanding of gradient-based attribution methods for Deep Neural Networks , 2017, ICLR.
[36] Arthur M. Lesk,et al. Introduction to protein architecture : the structural biologyof proteins , 2001 .
[37] Puteh Saad,et al. Remote protein homology detection and fold recognition using two-layer support vector machine classifiers , 2011, Comput. Biol. Medicine.
[38] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[39] Navdeep Jaitly,et al. Pointer Networks , 2015, NIPS.
[40] Genki Terashi,et al. Quality assessment methods for 3D protein structure models based on a residue-residue distance matrix prediction. , 2014, Chemical & pharmaceutical bulletin.
[41] Alex Fout,et al. Protein Interface Prediction using Graph Convolutional Networks , 2017, NIPS.
[42] Pierre Baldi,et al. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles , 2002, Proteins.
[43] P. Manikandan,et al. Enhanced Artificial Neural Network for Protein Fold Recognition and Structural Class Prediction , 2018, Gene Reports.
[44] B. Rost,et al. Improved prediction of protein secondary structure by use of sequence profiles and neural networks. , 1993, Proceedings of the National Academy of Sciences of the United States of America.
[45] Ron O. Dror,et al. Generalizable Protein Interface Prediction with End-to-End Learning , 2018, ArXiv.
[46] Jan Ramon,et al. Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel , 2015, Discovery Science.
[47] Andrzej Kloczkowski,et al. A global machine learning based scoring function for protein structure prediction , 2014, Proteins.
[48] Michael K. Gilson,et al. Virtual Screening of Molecular Databases Using a Support Vector Machine , 2005, J. Chem. Inf. Model..
[49] David Baker,et al. Ranking predicted protein structures with support vector regression , 2007, Proteins.
[50] David C. Jones,et al. GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. , 1999, Journal of molecular biology.
[51] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[52] Dong Xu,et al. Protein Structural Model Selection by Combining Consensus and Single Scoring Methods , 2013, PloS one.
[53] C Kooperberg,et al. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.
[54] Huan‐Xiang Zhou,et al. Prediction of protein interaction sites from sequence profile and residue neighbor list , 2001, Proteins.
[55] D T Jones,et al. Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.
[56] Zhen Li,et al. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2016, bioRxiv.
[57] Evangelia I. Zacharaki. Prediction of protein function using a deep convolutional neural network ensemble (#12536) , 2017 .
[58] Khaled Rasheed,et al. Classifying kinase conformations using a machine learning approach , 2017, BMC Bioinformatics.
[59] Rasiah Loganantharaj,et al. A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure , 2017 .
[60] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[61] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[62] Hod Lipson,et al. Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.
[63] Nikos Paragios,et al. A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors , 2016, IWBBIO.
[64] Jure Leskovec,et al. Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..
[65] Nikos Paragios,et al. EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation , 2017, PeerJ.
[66] Yoshua Bengio,et al. Deep convolutional networks for quality assessment of protein folds , 2018, Bioinform..
[67] Jie Hou,et al. DeepQA: improving the estimation of single protein model quality with deep belief networks , 2016, BMC Bioinformatics.
[68] Christoph A. Sotriffer,et al. SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes , 2013, J. Chem. Inf. Model..
[69] Leonidas J. Guibas,et al. Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).