| Training gans with optimism C Daskalakis, A Ilyas, V Syrgkanis, H Zeng arXiv preprint arXiv:1711.00141, 2017 | 589 | 2017 |
| Convolutional neural network architectures for predicting DNA–protein binding H Zeng, MD Edwards, G Liu, DK Gifford Bioinformatics 32 (12), i121-i127, 2016 | 555 | 2016 |
| Abundant contribution of short tandem repeats to gene expression variation in humans M Gymrek, T Willems, A Guilmatre, H Zeng, B Markus, S Georgiev, ... Nature genetics 48 (1), 22-29, 2016 | 374 | 2016 |
| A community computational challenge to predict the activity of pairs of compounds M Bansal, J Yang, C Karan, MP Menden, JC Costello, H Tang, G Xiao, ... Nature biotechnology 32 (12), 1213-1222, 2014 | 334 | 2014 |
| Antibody complementarity determining region design using high-capacity machine learning G Liu, H Zeng, J Mueller, B Carter, Z Wang, J Schilz, G Horny, ... Bioinformatics 36 (7), 2126-2133, 2020 | 165 | 2020 |
| Predicting the impact of non-coding variants on DNA methylation H Zeng, DK Gifford Nucleic Acid Research 45 (11), e99, 2017 | 105 | 2017 |
| Quantification of uncertainty in peptide-MHC binding prediction improves high-affinity peptide selection for therapeutic design H Zeng, DK Gifford Cell systems 9 (2), 159-166. e3, 2019 | 61 | 2019 |
| A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction Y Guo, K Tian, H Zeng, X Guo, DK Gifford Genome research 28 (6), 891-900, 2018 | 54 | 2018 |
| Predicting gene expression in massively parallel reporter assays: A comparative study A Kreimer, H Zeng, MD Edwards, Y Guo, K Tian, S Shin, R Welch, ... Human mutation 38 (9), 1240-1250, 2017 | 54 | 2017 |
| GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding H Zeng, T Hashimoto, DD Kang, DK Gifford Bioinformatics, 2015 | 50 | 2015 |
| DeepLigand: accurate prediction of MHC class I ligands using peptide embedding H Zeng, DK Gifford Bioinformatics 35 (14), i278-i283, 2019 | 43 | 2019 |
| Visualizing complex feature interactions and feature sharing in genomic deep neural networks G Liu, H Zeng, DK Gifford BMC bioinformatics 20, 1-14, 2019 | 26 | 2019 |
| A synergistic DNA logic predicts genome-wide chromatin accessibility T Hashimoto, RI Sherwood, DD Kang, N Rajagopal, AA Barkal, H Zeng, ... Genome research 26 (10), 1430-1440, 2016 | 20 | 2016 |
| CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods Genome biology 25 (1), 53, 2024 | 19 | 2024 |
| Mining TCGA data using Boolean implications S Sinha, EK Tsang, H Zeng, M Meister, DL Dill PloS one 9 (7), e102119, 2014 | 17 | 2014 |
| Accurate eQTL prioritization with an ensemble‐based framework H Zeng, MD Edwards, Y Guo, DK Gifford Human Mutation 38 (9), 1259-1265, 2017 | 16 | 2017 |
| Machine learning optimization of peptides for presentation by class II MHCs Z Dai, BD Huisman, H Zeng, B Carter, S Jain, ME Birnbaum, DK Gifford Bioinformatics 37 (19), 3160-3167, 2021 | 14 | 2021 |
| Machine learning based antibody design DK Gifford, H Zeng, G Liu US Patent App. 16/171,596, 2019 | 12 | 2019 |
| Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes R Atmaramani, T Dreossi, K Ford, L Gan, J Mitchell, S Tu, J Velayutham, ... bioRxiv, 2024.01. 04.574270, 2024 | 2 | 2024 |
| Discovering DNA motifs and genomic variants associated with DNA methylation H Zeng, DK Gifford bioRxiv, 073809, 2016 | 1 | 2016 |
