| 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 | 519 | 2015 |
| Post-transcriptional regulation across human tissues A Franks, E Airoldi, N Slavov PLoS computational biology 13 (5), e1005535, 2017 | 211 | 2017 |
| 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 | 208 | 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 | 161 | 2018 |
| Characterizing the spatial structure of defensive skill in professional basketball A Franks, A Miller, L Bornn, K Goldsberry | 151 | 2015 |
| Flexible sensitivity analysis for observational studies without observable implications AM Franks, A D’Amour, A Feller Journal of the American Statistical Association, 2020 | 121 | 2020 |
| Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments L Gatto, R Aebersold, J Cox, V Demichev, J Derks, E Emmott, AM Franks, ... Nature methods 20 (3), 375-386, 2023 | 103 | 2023 |
| Counterpoints: Advanced defensive metrics for nba basketball A Franks, A Miller, L Bornn, K Goldsberry 9th annual MIT sloan sports analytics conference, Boston, MA 10, 2015 | 103 | 2015 |
| Modeling player and team performance in basketball Z Terner, A Franks Annual Review of Statistics and Its Application 8 (1), 1-23, 2021 | 80 | 2021 |
| DART-ID increases single-cell proteome coverage AT Chen, A Franks, N Slavov PLoS computational biology 15 (7), e1007082, 2019 | 68 | 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 | 59 | 2016 |
| A metabolomic aging clock using human cerebrospinal fluid N Hwangbo, X Zhang, D Raftery, H Gu, SC Hu, TJ Montine, JF Quinn, ... The Journals of Gerontology: Series A 77 (4), 744-754, 2022 | 37* | 2022 |
| 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 | 36 | 2015 |
| Shared subspace models for multi-group covariance estimation AM Franks, P Hoff Journal of Machine Learning Research 20 (171), 1-37, 2019 | 25 | 2019 |
| Non-standard conditionally specified models for non-ignorable missing data AM Franks, EM Airoldi, DB Rubin arXiv preprint arXiv:1603.06045, 2016 | 23* | 2016 |
| Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding J Zheng, A D'Amour, A Franks arXiv preprint arXiv:2102.09412, 2021 | 17 | 2021 |
| Deconfounding scores: Feature representations for causal effect estimation with weak overlap A D'Amour, A Franks arXiv preprint arXiv:2104.05762, 2021 | 14 | 2021 |
| 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 | 14 | 2017 |
| 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 | 14 | 2015 |
| Predictive modeling of Alzheimer’s and Parkinson’s disease using metabolomic and lipidomic profiles from cerebrospinal fluid N Hwangbo, X Zhang, D Raftery, H Gu, SC Hu, TJ Montine, JF Quinn, ... Metabolites 12 (4), 277, 2022 | 12 | 2022 |
Edoardo M AiroldiProfessor of Statistics & Data Science Temple University & PI, Harvard UniversityVerified email at fas.harvard.edu
