Alexander Franks
Alexander Franks
Department of Statistics and Applied Probability, University of California Santa Barbara
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
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
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
Post-transcriptional regulation across human tissues
A Franks, E Airoldi, N Slavov
PLoS computational biology 13 (5), e1005535, 2017
Characterizing the spatial structure of defensive skill in professional basketball
A Franks, A Miller, L Bornn, K Goldsberry
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
Flexible sensitivity analysis for observational studies without observable implications
AM Franks, A D’Amour, A Feller
Journal of the American Statistical Association, 2019
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
DART-ID increases single-cell proteome coverage
AT Chen, A Franks, N Slavov
PLoS computational biology 15 (7), e1007082, 2019
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
Modeling player and team performance in basketball
Z Terner, A Franks
Annual Review of Statistics and Its Application 8, 1-23, 2021
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
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
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
Non-standard conditionally specified models for non-ignorable missing data
AM Franks, EM Airoldi, DB Rubin
arXiv preprint arXiv:1603.06045, 2016
Shared subspace models for multi-group covariance estimation
AM Franks, P Hoff
Journal of Machine Learning Research 20 (171), 1-37, 2019
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
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
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
Deconfounding scores: Feature representations for causal effect estimation with weak overlap
A D'Amour, A Franks
arXiv preprint arXiv:2104.05762, 2021
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
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