| Reversible, specific, active aggregates of endogenous proteins assemble upon heat stress EWJ Wallace, JL Kear-Scott, EV Pilipenko, MH Schwartz, PR Laskowski, ... Cell 162 (6), 1286-1298, 2015 | 255 | 2015 |
| Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast G Csárdi, A Franks, DS Choi, EM Airoldi, DA Drummond PLoS genetics 11 (5), e1005206, 2015 | 141 | 2015 |
| Characterizing the spatial structure of defensive skill in professional basketball A Franks, A Miller, L Bornn, K Goldsberry The Annals of Applied Statistics 9 (1), 94-121, 2015 | 86 | 2015 |
| Post-transcriptional regulation across human tissues A Franks, E Airoldi, N Slavov PLoS computational biology 13 (5), e1005535, 2017 | 78 | 2017 |
| Counterpoints: Advanced defensive metrics for nba basketball A Franks, A Miller, L Bornn, K Goldsberry 9th Annual MIT Sloan Sports Analytics Conference, Boston, MA, 2015 | 62 | 2015 |
| The companion dog as a model for human aging and mortality JM Hoffman, KE Creevy, A Franks, DG O'Neill, DEL Promislow Aging Cell 17 (3), e12737, 2018 | 37 | 2018 |
| A mixture-of-modelers approach to forecasting NCAA tournament outcomes LH Yuan, A Liu, A Yeh, A Kaufman, A Reece, P Bull, A Franks, S Wang, ... Journal of Quantitative Analysis in Sports 11 (1), 13-27, 2015 | 26 | 2015 |
| Flexible sensitivity analysis for observational studies without observable implications AM Franks, A D’Amour, A Feller Journal of the American Statistical Association, 1-33, 2019 | 22 | 2019 |
| Meta-analytics: tools for understanding the statistical properties of sports metrics AM Franks, A D’Amour, D Cervone, L Bornn Journal of Quantitative Analysis in Sports 12 (4), 151-165, 2016 | 18 | 2016 |
| DART-ID increases single-cell proteome coverage AT Chen, A Franks, N Slavov PLoS computational biology 15 (7), e1007082, 2019 | 14 | 2019 |
| Estimating a structured covariance matrix from multilab measurements in high-throughput biology AM Franks, G Csárdi, DA Drummond, EM Airoldi Journal of the American Statistical Association 110 (509), 27-44, 2015 | 12 | 2015 |
| Shared Subspace Models for Multi-Group Covariance Estimation. AM Franks, P Hoff Journal of Machine Learning Research 20 (171), 1-37, 2019 | 8 | 2019 |
| Non-standard conditionally specified models for non-ignorable missing data AM Franks, EM Airoldi, DB Rubin arXiv preprint arXiv:1603.06045, 2016 | 7 | 2016 |
| Studying basketball through the lens of player tracking data L Bornn, D Cervone, A Franks, A Miller Handbook of statistical methods and analyses in sports, 261-286, 2017 | 5 | 2017 |
| A Solution to the Challenge of Optimization on''Golf-Course''-Like Fitness Landscapes HPM Melo, A Franks, AA Moreira, D Diermeier, JS Andrade Jr, ... PloS one 8 (11), e78401, 2013 | 3 | 2013 |
| Modeling player and team performance in basketball Z Terner, A Franks Annual Review of Statistics and Its Application 8, 2020 | 2 | 2020 |
| Refining cellular pathway models using an ensemble of heterogeneous data sources AM Franks, F Markowetz, EM Airoldi The Annals of Applied Statistics 12 (3), 1361-1384, 2018 | 1* | 2018 |
| Reducing Subspace Models for Large-Scale Covariance Regression A Franks arXiv preprint arXiv:2010.00503, 2020 | | 2020 |
| Testing the adaptive value of sporulation in budding yeast using experimental evolution KM Thomasson, A Franks, H Teotónio, SR Proulx bioRxiv, 2020 | | 2020 |
| Quantifying Sources of Variation in High-throughput Biology AM Franks | | 2015 |
Edoardo M AiroldiProfessor of Statistics & Data Science, Temple University & PI, Harvard UniversityVerified email at fas.harvard.edu
