| Evaluating deep learning for predicting epigenomic profiles S Toneyan, Z Tang, PK Koo Nature machine intelligence 4 (12), 1088-1100, 2022 | 38 | 2022 |
| EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations NK Lee, Z Tang, S Toneyan, PK Koo Genome Biology 24 (1), 105, 2023 | 13 | 2023 |
| Current approaches to genomic deep learning struggle to fully capture human genetic variation Z Tang, S Toneyan, PK Koo Nature Genetics 55 (12), 2021-2022, 2023 | 7 | 2023 |
| Selecting deep neural networks that yield consistent attribution-based interpretations for genomics A Majdandzic, C Rajesh, Z Tang, S Toneyan, EL Labelson, RK Tripathy, ... Machine Learning in Computational Biology, 131-149, 2022 | 6 | 2022 |
| Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility RK Kawaguchi, Z Tang, S Fischer, R Tripathy, PK Koo, J Gillis BioRxiv, 2021.04. 01.438068, 2021 | 4* | 2021 |
| Evaluating the representational power of pre-trained DNA language models for regulatory genomics Z Tang, PK Koo bioRxiv, 2024 | 3 | 2024 |
| Building foundation models for regulatory genomics requires rethinking large language models Z Tang, PK Koo Proceedings of the ICML Workshop on Computational Biology, 2023 | 1 | 2023 |
| Exploring the Representational Power of Genomic Deep Learning Models Z Tang Cold Spring Harbor Laboratory, 2024 | | 2024 |
| Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion A Sarkar, Z Tang, C Zhao, P Koo bioRxiv, 2024.05. 23.595630, 2024 | | 2024 |
