| A cross-platform toolkit for mass spectrometry and proteomics MC Chambers, B Maclean, R Burke, D Amodei, DL Ruderman, ... Nature biotechnology 30 (10), 918-920, 2012 | 3221 | 2012 |
| Statistics of natural images: Scaling in the woods D Ruderman, W Bialek Advances in neural information processing systems 6, 1993 | 1339 | 1993 |
| Statistics of natural images: Scaling in the woods DL Ruderman, W Bialek Physical review letters 73 (6), 814, 1994 | 1144 | 1994 |
| Statistics of cone responses to natural images: implications for visual coding DL Ruderman, TW Cronin, CC Chiao JOSA A 15 (8), 2036-2045, 1998 | 1037 | 1998 |
| Origins of scaling in natural images DL Ruderman Vision research 37 (23), 3385-3398, 1997 | 458 | 1997 |
| Translation-invariant orientation tuning in visual “complex” cells could derive from intradendritic computations BW Mel, DL Ruderman, KA Archie Journal of Neuroscience 18 (11), 4325-4334, 1998 | 189 | 1998 |
| Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains N Naik, A Madani, A Esteva, NS Keskar, MF Press, D Ruderman, DB Agus, ... Nature communications 11 (1), 5727, 2020 | 184 | 2020 |
| Learning lie groups for invariant visual perception R Rao, D Ruderman Advances in neural information processing systems 11, 1998 | 137 | 1998 |
| Anterior gradient 2 (AGR2): blood‐based biomarker elevated in metastatic prostate cancer associated with the neuroendocrine phenotype K Kani, PD Malihi, Y Jiang, H Wang, Y Wang, DL Ruderman, DB Agus, ... The Prostate 73 (3), 306-315, 2013 | 85 | 2013 |
| Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images RR Rawat, I Ortega, P Roy, F Sha, D Shibata, D Ruderman, DB Agus Scientific reports 10 (1), 7275, 2020 | 79 | 2020 |
| Statistics of natural images. Network: Computation inNeural Systems 5: 51748.[aHB](1997) Origins of scaling in natural images DL Ruderman Vision Research 37, 338598, 1994 | 58 | 1994 |
| Estimation of errors in luminance signals encoded by primate retina resulting from sampling of natural images with red and green cones D Osorio, DL Ruderman, TW Cronin JOSA A 15 (1), 16-22, 1998 | 50 | 1998 |
| Designing receptive fields for highest fidelity DL Ruderman Network: Computation in neural systems 5 (2), 147, 1994 | 48 | 1994 |
| Optimal sampling of natural images: a design principle for the visual system W Bialek, D Ruderman, A Zee Advances in neural information processing systems 3, 1990 | 45 | 1990 |
| Multiscaling and information content of natural color images A Turiel, N Parga, DL Ruderman, TW Cronin Physical Review E 62 (1), 1138, 2000 | 38 | 2000 |
| Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens RR Rawat, D Ruderman, P Macklin, DL Rimm, DB Agus NPJ Breast Cancer 4 (1), 32, 2018 | 33 | 2018 |
| Seeing beyond the Nyquist limit DL Ruderman, W Bialek Neural computation 4 (5), 682-690, 1992 | 29 | 1992 |
| mTORC2 contributes to the metabolic reprogramming in EGFR tyrosine-kinase inhibitor resistant cells in non-small cell lung cancer CT Chiang, AN Demetriou, N Ung, N Choudhury, K Ghaffarian, ... Cancer letters 434, 152-159, 2018 | 25 | 2018 |
| Single cell dynamic phenotyping K Patsch, CL Chiu, M Engeln, DB Agus, P Mallick, SM Mumenthaler, ... Scientific reports 6 (1), 34785, 2016 | 22 | 2016 |
| Monitoring dynamic cytotoxic chemotherapy response in castration-resistant prostate cancer using plasma cell-free DNA (cfDNA) K Patsch, N Matasci, A Soundararajan, P Diaz, DB Agus, D Ruderman, ... BMC Research Notes 12, 1-7, 2019 | 21 | 2019 |
