| 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, 2018 | 2118 | 2018 |
| Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers J Gao, J Lanchantin, ML Soffa, Y Qi SPW, 2018 | 754 | 2018 |
| General Multi-label Image Classification with Transformers J Lanchantin, T Wang, V Ordonez, Y Qi CVPR, 2020 | 325 | 2020 |
| DeepChrome: Deep-Learning for Predicting Gene Expression from Histone Modifications R Singh, J Lanchantin, G Robins, Y Qi Bioinformatics, 2016 | 297 | 2016 |
| Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks J Lanchantin, R Singh, Z Lin, B Wang, Y Qi PSB, 2016 | 163 | 2016 |
| Reevaluating Adversarial Examples in Natural Language J Morris, E Lifland, J Lanchantin, Y Ji, Y Qi EMNLP Findings, 2020 | 115 | 2020 |
| Deep Motif: Visualizing Genomic Sequence Classifications J Lanchantin, R Singh, Z Lin, Y Qi ICLR (workshop track), 2016 | 101 | 2016 |
| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin R Singh, J Lanchantin, A Sekhon, Y Qi NeurIPS, 2017 | 90 | 2017 |
| MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction Z Lin, J Lanchantin, Y Qi AAAI, 2016 | 54 | 2016 |
| Neural Message Passing for Multi-Label Classification J Lanchantin, A Sekhon, Y Qi ECML, 2019 | 41 | 2019 |
| Time and Space Complexity of Graph Convolutional Networks D Blakely, J Lanchantin, Y Qi Preprint, 2021 | 39 | 2021 |
| Gakco: a fast gapped k-mer string kernel using counting R Singh, A Sekhon, K Kowsari, J Lanchantin, B Wang, Y Qi ECML, 2017 | 29 | 2017 |
| Learning to Reason and Memorize with Self-Notes J Lanchantin, S Toshniwal, J Weston, A Szlam, S Sukhbaatar NeurIPS, 2023 | 20 | 2023 |
| Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data J Lanchantin, Y Qi Bioinformatics, 2020 | 15 | 2020 |
| Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences J Lanchantin, T Weingarten, A Sekhon, C Miller, Y Qi ACM-BCB, 2020 | 13* | 2020 |
| FastSK: fast sequence analysis with gapped string kernels D Blakely, E Collins, R Singh, A Norton, J Lanchantin, Y Qi Bioinformatics, 2020 | 7 | 2020 |
| Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification J Lanchantin, A Sekhon, R Singh, Y Qi arXiv, 2017 | 6 | 2017 |
| A Data Source for Reasoning Embodied Agents J Lanchantin, S Sukhbaatar, G Synnaeve, Y Sun, K Srinet, A Szlam AAAI, 2023 | 4 | 2023 |
| Transfer string kernel for cross-context DNA-protein binding prediction R Singh, J Lanchantin, G Robins, Y Qi TCBB, 2016 | 4 | 2016 |
| Exploring the naturalness of buggy code with recurrent neural networks J Lanchantin, J Gao arXiv, 2016 | 4 | 2016 |
Yanjun QiFaculty @UVa CS / PhD @CMU / DATA Scholar@ NIH/ Principal Applied Scientist @ AWSVerified email at virginia.edu
