| A deep neural network for predicting and engineering alternative polyadenylation N Bogard, J Linder, AB Rosenberg, G Seelig Cell 178 (1), 91-106. e23, 2019 | 180 | 2019 |
| A generative neural network for maximizing fitness and diversity of synthetic DNA and protein sequences J Linder, N Bogard, AB Rosenberg, G Seelig Cell systems 11 (1), 49-62. e16, 2020 | 105* | 2020 |
| Fast activation maximization for molecular sequence design J Linder, G Seelig BMC bioinformatics 22, 1-20, 2021 | 52* | 2021 |
| Deciphering the impact of genetic variation on human polyadenylation using APARENT2 J Linder, SE Koplik, A Kundaje, G Seelig Genome biology 23 (1), 232, 2022 | 22 | 2022 |
| Interpreting neural networks for biological sequences by learning stochastic masks J Linder, A La Fleur, Z Chen, A Ljubetič, D Baker, S Kannan, G Seelig Nature machine intelligence 4 (1), 41-54, 2022 | 19 | 2022 |
| Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation J Linder, D Srivastava, H Yuan, V Agarwal, DR Kelley Biorxiv, 2023.08. 30.555582, 2023 | 18 | 2023 |
| Robust digital molecular design of binarized neural networks J Linder, YJ Chen, D Wong, G Seelig, L Ceze, K Strauss 27th International Conference on DNA Computing and Molecular Programming …, 2021 | 6 | 2021 |
| Rewriting regulatory DNA to dissect and reprogram gene expression GE Martyn, MT Montgomery, H Jones, K Guo, BR Doughty, J Linder, ... bioRxiv, 2023 | 4 | 2023 |
| CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators MH Kowalski, HH Wessels, J Linder, S Choudhary, A Hartman, Y Hao, ... BioRxiv, 2023 | 4 | 2023 |
| Neural networks implemented with DSD circuits K Strauss, L Ceze, JSA Linder US Patent 11,704,575, 2023 | 3 | 2023 |
| Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning S Castillo-Hair, S Fedak, B Wang, J Linder, K Havens, M Certo, G Seelig Nature Communications 15 (1), 5284, 2024 | 2 | 2024 |
| The anticancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3′ processing machinery L Liu, AM Yu, X Wang, LV Soles, X Teng, Y Chen, Y Yoon, KSK Sarkan, ... Nature Structural & Molecular Biology 30 (12), 1947-1957, 2023 | 2 | 2023 |
| Efficient inference of nonlinear feature attributions with scrambling neural networks J Linder, A LaFleur, S Kannan, Z Chen, A Ljubetic, D Baker, G Seelig Proceedings of the 2nd Conference on Machine Learning in Computational Biology, 2020 | 1 | 2020 |
| Modeling the intronic regulation of Alternative Splicing using Deep Convolutional Neural Nets J Linder | 1 | 2015 |
| Multiplexed single-cell characterization of alternative polyadenylation regulators MH Kowalski, HH Wessels, J Linder, C Dalgarno, I Mascio, S Choudhary, ... Cell, 2024 | | 2024 |
| Neural networks implemented with dsd circuits K Strauss, L Ceze, JSA Linder US Patent App. 18/204,363, 2023 | | 2023 |
| Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning S Fedak, B Wang, J Linder, K Havens, M Certo, G Seelig | | 2023 |
| Deciphering the Impact of Genetic Variation on Human Polyadenylation J Linder, A Kundaje, G Seelig bioRxiv, 2022.05. 09.491198, 2022 | | 2022 |
| Predicting, Engineering and Interpreting Gene Regulatory Sequences and Proteins with Deep Learning JSA Linder University of Washington, 2021 | | 2021 |
| Interpreting Neural Networks for Biological Sequences by Learning Masks J Linder, A La Fleur, S Kannan, Z Chen, A Ljubetič, D Baker, G Seelig | | |
