DeepTox: Toxicity Prediction using Deep Learning
暂无分享,去创建一个
Günter Klambauer | Andreas Mayr | Sepp Hochreiter | Thomas Unterthiner | S. Hochreiter | Thomas Unterthiner | G. Klambauer | Andreas Mayr | Sepp Hochreiter
[1] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[2] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[3] H. B. Barlow,et al. Unsupervised Learning , 1989, Neural Computation.
[4] Sepp Hochreiter,et al. Untersuchungen zu dynamischen neuronalen Netzen , 1991 .
[5] Juan M. Luco,et al. QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives , 1997, J. Chem. Inf. Comput. Sci..
[6] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[7] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[8] Terrence J. Sejnowski,et al. Unsupervised Learning , 2018, Encyclopedia of GIS.
[9] Gregory W. Kauffman,et al. QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors , 2001, J. Chem. Inf. Comput. Sci..
[10] R. Evans,et al. Nuclear receptors and lipid physiology: opening the X-files. , 2001, Science.
[11] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[12] Hisashi Kashima,et al. Marginalized Kernels Between Labeled Graphs , 2003, ICML.
[13] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[14] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[15] Andreas Bender,et al. Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier , 2004, J. Chem. Inf. Model..
[16] Xiaoyang Xia,et al. Classification of kinase inhibitors using a Bayesian model. , 2004, Journal of medicinal chemistry.
[17] H. Kashima,et al. Kernels for graphs , 2004 .
[18] J. Kazius,et al. Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.
[19] Tatsuya Akutsu,et al. Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines , 2005, J. Chem. Inf. Model..
[20] T. Ørntoft,et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis , 2005, Nature.
[21] Pierre Baldi,et al. Graph kernels for chemical informatics , 2005, Neural Networks.
[22] Jean-Philippe Vert,et al. The Pharmacophore Kernel for Virtual Screening with Support Vector Machines , 2006, J. Chem. Inf. Model..
[23] Sudhir A. Kulkarni,et al. Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation , 2006, J. Chem. Inf. Model..
[24] S. V. N. Vishwanathan,et al. Graph kernels , 2007 .
[25] Division on Earth. Toxicity Testing in the 21st Century: A Vision and a Strategy , 2007 .
[26] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[27] Yu-Dong Cai,et al. Support vector machine for SAR/QSAR of phenethyl-amines , 2007, Acta Pharmacologica Sinica.
[28] F. Grün,et al. Perturbed nuclear receptor signaling by environmental obesogens as emerging factors in the obesity crisis , 2007, Reviews in Endocrine and Metabolic Disorders.
[29] G. Labbe,et al. Drug‐induced liver injury through mitochondrial dysfunction: mechanisms and detection during preclinical safety studies , 2008, Fundamental & clinical pharmacology.
[30] Klaus Obermayer,et al. Molecule Kernels: A Descriptor- and Alignment-Free Quantitative Structure-Activity Relationship Approach , 2008, J. Chem. Inf. Model..
[31] M. Greenberg,et al. Toxicity Testing in the 21st Century , 2009, Risk analysis : an official publication of the Society for Risk Analysis.
[32] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[33] Victor Kuzmin,et al. Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity , 2009, J. Chem. Inf. Model..
[34] Karen Lowrie,et al. Toxicity testing in the 21st century. , 2009, Risk analysis : an official publication of the Society for Risk Analysis.
[35] Melvin E Andersen,et al. Toxicity testing in the 21st century: bringing the vision to life. , 2009, Toxicological sciences : an official journal of the Society of Toxicology.
[36] Rajat Raina,et al. Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.
[37] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[38] Klaus Obermayer,et al. A Maximum Common Subgraph Kernel Method for Predicting the Chromosome Aberration Test , 2010, J. Chem. Inf. Model..
[39] I. Rusyn,et al. Computational Toxicology: Realizing the Promise of the Toxicity Testing in the 21st Century , 2010, Environmental health perspectives.
[40] Ruili Huang,et al. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. , 2010, Drug discovery today.
[41] Rachid Darnag,et al. Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives. , 2010, European journal of medicinal chemistry.
[42] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[43] J. Bailar,et al. Toxicity Testing in the 21st Century: A Vision and a Strategy , 2010, Journal of toxicology and environmental health. Part B, Critical reviews.
[44] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[45] Ralph Kühne,et al. Tautomer Identification and Tautomer Structure Generation Based on the InChI Code , 2010, J. Chem. Inf. Model..
[46] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[47] Quoc V. Le,et al. On optimization methods for deep learning , 2011, ICML.
[48] Andreas Zell,et al. jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints , 2011, J. Cheminformatics.
[49] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[50] Andreas Zell,et al. Interpreting linear support vector machine models with heat map molecule coloring , 2011, J. Cheminformatics.
[51] Honglak Lee,et al. Unsupervised learning of hierarchical representations with convolutional deep belief networks , 2011, Commun. ACM.
[52] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[53] Yizeng Liang,et al. Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool , 2012 .
[54] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[55] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Yoshua Bengio,et al. Deep Learning for NLP (without Magic) , 2012, ACL.
[57] Dong Yu,et al. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[58] Luca Maria Gambardella,et al. Deep Big Multilayer Perceptrons for Digit Recognition , 2012, Neural Networks: Tricks of the Trade.
[59] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[60] Mitchell R. McGill,et al. Oxidant stress, mitochondria, and cell death mechanisms in drug-induced liver injury: Lessons learned from acetaminophen hepatotoxicity , 2012, Drug metabolism reviews.
[61] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[62] Luca Maria Gambardella,et al. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.
[63] I. Sagardia,et al. A new QSAR model, for angiotensin I-converting enzyme inhibitory oligopeptides. , 2013, Food chemistry.
[64] Dong-Sheng Cao,et al. ChemoPy: freely available python package for computational biology and chemoinformatics , 2013, Bioinform..
[65] Brian Kingsbury,et al. New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[66] K Mikael,et al. Deep Learning for NLP , 2013 .
[67] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[68] Navdeep Jaitly,et al. Multi-task Neural Networks for QSAR Predictions , 2014, ArXiv.
[69] P. Baldi,et al. Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.
[70] David M. Reif,et al. Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway , 2014, Scientific Reports.
[71] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[72] Ana L. Teixeira,et al. Prediction of human population responses to toxic compounds by a collaborative competition , 2015, Nature Biotechnology.
[73] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[74] Sepp Hochreiter,et al. Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map , 2015, Bioinform..
[75] Robert P. Sheridan,et al. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..
[76] Sepp Hochreiter,et al. Toxicity Prediction using Deep Learning , 2015, ArXiv.
[77] Andreas Mayr,et al. Deep Learning as an Opportunity in Virtual Screening , 2015 .
[78] Sepp Hochreiter,et al. Rectified Factor Networks , 2015, NIPS.
[79] Bie M. P. Verbist,et al. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. , 2015, Drug discovery today.
[80] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..