Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of the royal society interface 15 (141), 20170387, 2018 | 1987 | 2018 |
Feature squeezing: Detecting adversarial examples in deep neural networks W Xu, D Evans, Y Qi Network and Distributed Systems Security Symposium (NDSS) 2018, 2018 | 1955 | 2018 |
Random forest for bioinformatics Y Qi Ensemble machine learning: Methods and applications, 307-323, 2012 | 890 | 2012 |
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers J Gao, J Lanchantin, ML Soffa, Y Qi 1st DEEP LEARNING AND SECURITY WORKSHOP (DLS18), arXiv preprint arXiv:1801.04354, 2018 | 709 | 2018 |
Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp JX Morris, E Lifland, JY Yoo, J Grigsby, D Jin, Y Qi EMNLP 2021 / arXiv preprint arXiv:2005.05909, 2020 | 665 | 2020 |
Automatically evading classifiers W Xu, Y Qi, D Evans Proceedings of the 2016 network and distributed systems symposium 10, 2016 | 510 | 2016 |
Evaluation of different biological data and computational classification methods for use in protein interaction prediction Y Qi, Z Bar‐Joseph, J Klein‐Seetharaman Proteins: Structure, Function, and Bioinformatics 63 (3), 490-500, 2006 | 490 | 2006 |
Systems and methods for semi-supervised relationship extraction Y Qi, B Bai, X Ning, P Kuksa US Patent 8,874,432, 2014 | 353 | 2014 |
A critical assessment of Mus musculusgene function prediction using integrated genomic evidence L Peña-Castillo, M Tasan, CL Myers, H Lee, T Joshi, C Zhang, Y Guan, ... Genome biology 9, 1-19, 2008 | 299 | 2008 |
General multi-label image classification with transformers J Lanchantin, T Wang, V Ordonez, Y Qi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 291 | 2021 |
DeepChrome: deep-learning for predicting gene expression from histone modifications R Singh, J Lanchantin, G Robins, Y Qi Bioinformatics 32 (17), i639-i648, 2016 | 291 | 2016 |
Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning P Cascante-Bonilla, F Tan, Y Qi, V Ordonez AAAI 2021 / arXiv preprint arXiv:2001.06001, 2020 | 275 | 2020 |
Random forest similarity for protein-protein interaction prediction from multiple sources Y Qi, J Klein-Seetharaman, Z Bar-Joseph Biocomputing 2005, 531-542, 2005 | 260 | 2005 |
Cas9-chromatin binding information enables more accurate CRISPR off-target prediction MA R Singh, C Kuscu, A Quinlan, Y Qi Nucleic acids research, 2015 | 238 | 2015 |
Sentiment classification based on supervised latent n-gram analysis D Bespalov, B Bai, Y Qi, A Shokoufandeh Proceedings of the 20th ACM international conference on Information and …, 2011 | 196 | 2011 |
Prediction of interactions between HIV-1 and human proteins by information integration O Tastan, Y Qi, JG Carbonell, J Klein-Seetharaman Biocomputing 2009, 516-527, 2009 | 191 | 2009 |
Recurrent chimeric fusion RNAs in non-cancer tissues and cells M Babiceanu, F Qin, Z Xie, Y Jia, K Lopez, N Janus, L Facemire, S Kumar, ... Nucleic acids research 44 (6), 2859-2872, 2016 | 178 | 2016 |
Protein complex identification by supervised graph local clustering Y Qi, F Balem, C Faloutsos, J Klein-Seetharaman, Z Bar-Joseph Bioinformatics 24 (13), i250-i268, 2008 | 167 | 2008 |
Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks J Lanchantin, R Singh, B Wang, Y Qi Pacific Symposium on Biocomputing 2017, 254-265, 2017 | 162 | 2017 |
Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins Y Qi, O Tastan, JG Carbonell, J Klein-Seetharaman, J Weston Bioinformatics 26 (18), i645-i652, 2010 | 155 | 2010 |