| Molecular graph convolutions: moving beyond fingerprints S Kearnes, K McCloskey, M Berndl, V Pande, P Riley Journal of computer-aided molecular design 30, 595-608, 2016 | 1671 | 2016 |
| In silico labeling: predicting fluorescent labels in unlabeled images EM Christiansen, SJ Yang, DM Ando, A Javaherian, G Skibinski, S Lipnick, ... Cell 173 (3), 792-803. e19, 2018 | 583 | 2018 |
| Points-to analysis using BDDs M Berndl, O Lhoták, F Qian, L Hendren, N Umanee ACM SIGPLAN Notices 38 (5), 103-114, 2003 | 335 | 2003 |
| High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis S Tiwary, R Levy, P Gutenbrunner, F Salinas Soto, KK Palaniappan, ... Nature methods 16 (6), 519-525, 2019 | 230 | 2019 |
| Assessing microscope image focus quality with deep learning SJ Yang, M Berndl, D Michael Ando, M Barch, A Narayanaswamy, ... BMC bioinformatics 19, 1-9, 2018 | 159 | 2018 |
| Context threading: A flexible and efficient dispatch technique for virtual machine interpreters M Berndl, B Vitale, M Zaleski, AD Brown International Symposium on Code Generation and Optimization, 15-26, 2005 | 81 | 2005 |
| Improving phenotypic measurements in high-content imaging screens DM Ando, CY McLean, M Berndl BioRxiv, 161422, 2017 | 77 | 2017 |
| Machine learning guided aptamer refinement and discovery A Bashir, Q Yang, J Wang, S Hoyer, W Chou, C McLean, G Davis, Q Gong, ... Nature Communications 12 (1), 2366, 2021 | 72 | 2021 |
| Learning causal networks using inducible transcription factors and transcriptome‐wide time series SR Hackett, EA Baltz, M Coram, BJ Wranik, G Kim, A Baker, M Fan, ... Molecular systems biology 16 (3), e9174, 2020 | 48 | 2020 |
| Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts L Schiff, B Migliori, Y Chen, D Carter, C Bonilla, J Hall, M Fan, E Tam, ... Nature communications 13 (1), 1590, 2022 | 40* | 2022 |
| Dynamic profiling and trace cache generation M Berndl, L Hendren International Symposium on Code Generation and Optimization, 2003. CGO 2003 …, 2003 | 38* | 2003 |
| Discovery of complex oxides via automated experiments and data science L Yang, JA Haber, Z Armstrong, SJ Yang, K Kan, L Zhou, MH Richter, ... Proceedings of the National Academy of Sciences 118 (37), e2106042118, 2021 | 29 | 2021 |
| Applying deep neural network analysis to high-content image-based assays SJ Yang, SL Lipnick, NR Makhortova, S Venugopalan, M Fan, ... SLAS DISCOVERY: Advancing Life Sciences R&D 24 (8), 829-841, 2019 | 22 | 2019 |
| Processing cell images using neural networks PC Nelson, EM Christiansen, M Berndl, M Frumkin US Patent 11,915,134, 2024 | 17* | 2024 |
| Selection and display of online advertisements JT Wills, D Leslie-Cook, M Berndl, G Aggarwal, D Liu, HH Nash Jr, ... US Patent 8,682,720, 2014 | 16 | 2014 |
| Advertisement selection K Gopalratnam, L Ertöz, M Sussman, M Berndl, D Liu, S Ramaswamy, ... US Patent 8,423,405, 2013 | 14 | 2013 |
| Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock S Ahadi, KA Wilson, B Babenko, CY McLean, D Bryant, O Pritchard, ... Elife 12, e82364, 2023 | 12 | 2023 |
| ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes JM Hong, M Gibbons, A Bashir, D Wu, S Shao, Z Cutts, M Chavarha, ... Iscience 25 (1), 2022 | 9 | 2022 |
| Analysis of perturbed subjects using semantic embeddings DM Ando, M Berndl, L Yang, M Dimon US Patent 10,769,501, 2020 | 9 | 2020 |
| It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets S Venugopalan, A Narayanaswamy, S Yang, A Geraschenko, S Lipnick, ... arXiv preprint arXiv:1912.07661, 2019 | 9 | 2019 |
