| Convolutional neural network architectures for predicting DNA–protein binding H Zeng, MD Edwards, G Liu, DK Gifford Bioinformatics 32 (12), i121-i127, 2016 | 486 | 2016 |
| Training gans with optimism C Daskalakis, A Ilyas, V Syrgkanis, H Zeng arXiv preprint arXiv:1711.00141, 2017 | 479 | 2017 |
| 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 | 315 | 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 | 290 | 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 | 112 | 2020 |
| Predicting the impact of non-coding variants on DNA methylation H Zeng, DK Gifford Nucleic Acid Research 45 (11), e99, 2017 | 91 | 2017 |
| 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 | 47 | 2018 |
| GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding H Zeng, T Hashimoto, DD Kang, DK Gifford Bioinformatics, 2015 | 45 | 2015 |
| 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 | 42 | 2019 |
| 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 | 41 | 2017 |
| DeepLigand: accurate prediction of MHC class I ligands using peptide embedding H Zeng, DK Gifford Bioinformatics 35 (14), i278-i283, 2019 | 35 | 2019 |
| Visualizing complex feature interactions and feature sharing in genomic deep neural networks G Liu, H Zeng, DK Gifford BMC bioinformatics 20 (1), 1-14, 2019 | 23 | 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 | 19 | 2016 |
| Mining TCGA data using Boolean implications S Sinha, EK Tsang, H Zeng, M Meister, DL Dill PloS one 9 (7), e102119, 2014 | 15 | 2014 |
| Accurate eQTL prioritization with an ensemble‐based framework H Zeng, MD Edwards, Y Guo, DK Gifford Human mutation 38 (9), 1259-1265, 2017 | 13 | 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 | 9 | 2021 |
| Machine learning based antibody design DK Gifford, H Zeng, G Liu US Patent App. 16/171,596, 2019 | 8 | 2019 |
| Discovering DNA motifs and genomic variants associated with DNA methylation H Zeng, DK Gifford bioRxiv, 073809, 2016 | 1 | 2016 |
| Whole Genome Regulatory Variant Evaluation for Transcription Factor Binding H Zeng, T Hashimoto, DD Kang, DK Gifford bioRxiv, 017392, 2015 | 1 | 2015 |
| Machine learning methods for detailed characterization of TGF-beta-induced signatures in a large iPSC-derived hepatic stellate cell cohort P Stanitsas, KM Liu, L Kategaya, K Haston, A Lee, S Mohammadi, ... Journal of Hepatology 77, S37-S38, 2022 | | 2022 |
