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Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Contextual correlates of synonymy
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The properties of known drugs. 1. molecular frameworks
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Gene ontology: tool for the unification of biology
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Extended-connectivity fingerprints
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The Weisfeiler-Lehman method and graph isomorphism testing
Brendan L Douglas · 2011
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Effectiveness of 2d fingerprints for scaffold hopping
Eleanor J Gardiner, John D Holliday, Caroline O’Dowd, and Peter Willett · 2011
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ChEMBL: a large-scale bioactivity database for drug discovery
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Comparison of random forest and pipeline pilot naïve bayes in prospective QSAR predictions
Bin Chen, Robert P. Sheridan, Viktor Hornak, and Johannes H. Voigt · 2012
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A bayesian approach to in silico blood-brain barrier penetration modeling
Ines Filipa Martins, Ana L Teixeira, Luis Pinheiro, and Andre O Falcao · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery
Paul A. Novick, Oscar F. Ortiz, Jared Poelman, Amir Y. Abdulhay, and Vijay S. Pande · 2013
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Time-split cross-validation as a method for estimating the goodness of prospective prediction
Robert P. Sheridan · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Tox21 data challenge 2014, 2014
Tox21 · 2014
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EgoNet: identification of human disease ego-network modules
Rendong Yang, Yun Bai, Zhaohui Qin, and Tianwei Yu · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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The sider database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 2015
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Zinc 15 – ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Goatools: A Python library for gene ontology analyses
DV Klopfenstein, Liangsheng Zhang, Brent S Pedersen, Fidel Ramírez, Alex Warwick Vesztrocy, Aurélien Naldi, Christopher J Mungall, Jeffrey M Yunes, Olga Botvinnik, Mark Weigel, et al · 2018
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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter · 2018
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Pre-training graph neural networks with kernels
Nicolò Navarin, Dinh V Tran, and Alessandro Sperduti · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs
Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Toxcast chemical landscape: Paving the road to 21st century toxicology
Ann M. Richard, Richard S. Judson, Keith A. Houck, Christopher M. Grulke, Patra Volarath, Inthirany Thillainadarajah, Chihae Yang, James Rathman, Matthew T. Martin, John F. Wambaugh, Thomas B. Knudsen, Jayaram Kancherla, Kamel Mansouri, Grace Patlewicz, Antony J. Williams, Stephen B. Little, Kevin M. Crofton, and Russell S. Thomas · 2016
Cited alongside, same era.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Learning atoms for materials discovery
Quan Zhou, Peizhe Tang, Shenxiu Liu, Jinbo Pan, Qimin Yan, and Shou-Cheng Zhang · 2018
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Prioritizing network communities
Marinka Zitnik, Rok Sosic, and Jure Leskovec · 2018
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Unsupervised inductive whole-graph embedding by preserving graph proximity
Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Fast graph representation learning with Pytorch Geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Graph warp module: An auxiliary module for boosting the power of graph neural networks
Katsuhiko Ishiguro, Shin-ichi Maeda, and Masanori Koyama · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande · 2019
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Unsupervised word embeddings capture latent knowledge from materials science literature
Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
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Deep graph infomax
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Data denoising with transfer learning in single-cell transcriptomics
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, and Nancy R Zhang · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Position-aware graph neural networks
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Evolution of resilience in protein interactomes across the tree of life
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