Alexander Franks

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Edoardo M AiroldiProfessor of Statistics & Data Science Temple University & PI, Harvard UniversityCorreu electrònic verificat a fas.harvard.edu
Luke BornnCo-Founder and Chief Scientist, Zelus AnalyticsCorreu electrònic verificat a stat.harvard.edu
D. Allan DrummondAssociate Professor, Biochemistry and Molecular Biology, Genetic MedicineCorreu electrònic verificat a uchicago.edu
Nikolai SlavovNortheastern University, Harvard University, MIT, Princeton UniversityCorreu electrònic verificat a fas.harvard.edu
Avi FellerUC BerkeleyCorreu electrònic verificat a berkeley.edu
Andrew C. MillerResearch Scientist, AppleCorreu electrònic verificat a apple.com
Edward W. J. WallaceSenior Lecturer in RNA Systems Biology, School of Biological Sciences, University of EdinburghCorreu electrònic verificat a ed.ac.uk
Gábor CsárdiPosit PBCCorreu electrònic verificat a posit.co
Eli Ben-MichaelAssistant Professor, Carnegie Mellon UniversityCorreu electrònic verificat a cmu.edu
Donald B RubinYCMS Tsinghua; Senior Research Fellow at Fox / Temple U, Emeritus at Harvard UCorreu electrònic verificat a temple.edu
André Auto MoreiraProfessor of Physics, Universidade Federal do CearaCorreu electrònic verificat a fisica.ufc.br
Daniel DiermeierChancellor and Distinguished University Professor, Vanderbilt UniversityCorreu electrònic verificat a vanderbilt.edu
David ChoiNASA Postdoctoral Fellow, Goddard Space Flight CenterCorreu electrònic verificat a nasa.gov

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Alexander Franks

Department of Statistics and Applied Probability, University of California Santa Barbara

Correu electrònic verificat a pstat.ucsb.edu - Pàgina d'inici


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
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	533	2015
Post-transcriptional regulation across human tissues A Franks, E Airoldi, N Slavov PLoS computational biology 13 (5), e1005535, 2017	219	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	209	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	171	2018
Characterizing the spatial structure of defensive skill in professional basketball A Franks, A Miller, L Bornn, K Goldsberry	152	2015
Flexible sensitivity analysis for observational studies without observable implications AM Franks, A D’Amour, A Feller Journal of the American Statistical Association, 2020	127	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	115	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	106	2015
Modeling player and team performance in basketball Z Terner, A Franks Annual Review of Statistics and Its Application 8 (1), 1-23, 2021	78	2021
DART-ID increases single-cell proteome coverage AT Chen, A Franks, N Slavov PLoS computational biology 15 (7), e1007082, 2019	71	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	40*	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	37	2015
Shared subspace models for multi-group covariance estimation AM Franks, P Hoff Journal of Machine Learning Research 20 (171), 1-37, 2019	27	2019
Non-standard conditionally specified models for non-ignorable missing data AM Franks, EM Airoldi, DB Rubin arXiv preprint arXiv:1603.06045, 2016	25*	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	20	2021
Deconfounding scores: Feature representations for causal effect estimation with weak overlap A D'Amour, A Franks arXiv preprint arXiv:2104.05762, 2021	16	2021
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	13	2022
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	13	2017

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