| 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 | 2315 | 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 | 2246 | 2018 |
| Random forest for bioinformatics Y Qi Ensemble machine learning: Methods and applications, 307-323, 2012 | 1045 | 2012 |
| 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 | 850 | 2020 |
| 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 | 840 | 2018 |
| Automatically evading classifiers W Xu, Y Qi, D Evans Proceedings of the 2016 network and distributed systems symposium 10, 2016 | 537 | 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 | 507 | 2006 |
| 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 | 375 | 2021 |
| Systems and methods for semi-supervised relationship extraction Y Qi, B Bai, X Ning, P Kuksa US Patent 8,874,432, 2014 | 372 | 2014 |
| 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 | 370 | 2020 |
| DeepChrome: deep-learning for predicting gene expression from histone modifications R Singh, J Lanchantin, G Robins, Y Qi Bioinformatics 32 (17), i639-i648, 2016 | 314 | 2016 |
| 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 | 304 | 2008 |
| Random forest similarity for protein-protein interaction prediction from multiple sources Y Qi, J Klein-Seetharaman, Z Bar-Joseph Biocomputing 2005, 531-542, 2005 | 265 | 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 | 251 | 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 | 204 | 2011 |
| 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 | 201 | 2016 |
| 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 | 195 | 2009 |
| 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 | 179 | 2017 |
| 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 |
| Towards Improving Adversarial Training of NLP Models JY Yoo, Y Qi (EMNLP21) Empirical Methods in Natural Language Processing: EMNLP / arXiv …, 2021 | 155 | 2021 |
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