Yanjun Qi
Yanjun Qi
Faculty @UVa CS / PhD @CMU / DATA Scholar@ NIH/ Principal Applied Scientist @ AWS
Verified email at - Homepage
Cited by
Cited by
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
Feature squeezing: Detecting adversarial examples in deep neural networks
W Xu, D Evans, Y Qi
Network and Distributed Systems Security Symposium (NDSS) 2018, 2018
Random forest for bioinformatics
Y Qi
Ensemble machine learning: Methods and applications, 307-323, 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
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
Automatically evading classifiers
W Xu, Y Qi, D Evans
Proceedings of the 2016 network and distributed systems symposium 10, 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
Systems and methods for semi-supervised relationship extraction
Y Qi, B Bai, X Ning, P Kuksa
US Patent 8,874,432, 2014
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
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
DeepChrome: deep-learning for predicting gene expression from histone modifications
R Singh, J Lanchantin, G Robins, Y Qi
Bioinformatics 32 (17), i639-i648, 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
Random forest similarity for protein-protein interaction prediction from multiple sources
Y Qi, J Klein-Seetharaman, Z Bar-Joseph
Biocomputing 2005, 531-542, 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
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
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
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
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
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
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
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