Alexander M. Franks

发表

Post-transcriptional regulation across human tissues

Edoardo M. Airoldi, Alexander Franks, Nikolai Slavov, 2015, bioRxiv.

Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

E. Airoldi, Alexander M. Franks, Gábor Csárdi, 2014, bioRxiv.

Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology

E. Airoldi, Alexander M. Franks, Gábor Csárdi, 2015, Journal of the American Statistical Association.

A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes

José S. Andrade, Daniel Diermeier, André A. Moreira, 2013, PloS one.

Accounting for experimental noise reveals that transcription dominates control of steady-state protein levels in yeast

E. Airoldi, Alexander M. Franks, Gábor Csárdi, 2014 .

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding

A. D'Amour, Alexander M. Franks, Jia-wen Zheng, 2021, AISTATS.

Nonstandard conditionally specified models for nonignorable missing data

D. Rubin, E. Airoldi, Alexander M. Franks, 2016, Proceedings of the National Academy of Sciences.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

E. Airoldi, Alexander M. Franks, Gábor Csárdi, 2014, bioRxiv.

DART-ID increases single-cell proteome coverage

Alexander M. Franks, N. Slavov, A. Chen, 2018, bioRxiv.

Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

T. Montine, J. Quinn, Alexander M. Franks, 2022, Metabolites.

Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

A. D'Amour, Alexander M. Franks, Jia-wen Zheng, 2022, 2208.06552.

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

David H Perlman, Aleksandra A. Petelski, R. Aebersold, 2022, Nature Methods.

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

A. D'Amour, Alexander M. Franks, Jia-wen Zheng, 2021, 2102.09412.

Flexible Sensitivity Analysis for Observational Studies Without Observable Implications

Alexander D'Amour, Avi Feller, Alexander Franks, 2018, Journal of the American Statistical Association.

Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress

Michael H. Schwartz, Joshua A. Riback, E. Airoldi, 2015, Cell.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES.

Edoardo M. Airoldi, Florian Markowetz, Alexander Franks, 2018, The annals of applied statistics.

Estimating cellular pathways from an ensemble of heterogeneous data sources

Edoardo Airoldi, Florian Markowetz, Alexander Franks, 2014, bioRxiv.

The companion dog as a model for human aging and mortality

Jessica M. Hoffman, Alexander M. Franks, D. Promislow, 2018, Aging cell.

Nonstandard conditionally specified models for nonignorable missing data

Edoardo M Airoldi, Donald B Rubin, Alexander M Franks, 2020, Proceedings of the National Academy of Sciences.

Extensive post-transcriptional regulation across human tissues

E. Airoldi, Alexander M. Franks, N. Slavov, 2015 .

Learning Gaussian Graphical Models with Latent Confounders

Sang-Yun Oh, Alexander Franks, Ke Wang, 2021, 2105.06600.

Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

A. D'Amour, Alexander M. Franks, Jia-wen Zheng, 2022, Journal of the American Statistical Association.

A metabolomic aging clock using human CSF.

Q. Fei, T. Montine, J. Quinn, 2021, The journals of gerontology. Series A, Biological sciences and medical sciences.

Shared Subspace Models for Multi-Group Covariance Estimation

Alexander Franks, Peter Hoff, Alexander M. Franks, 2016, J. Mach. Learn. Res..

An aging clock using metabolomic CSF

Q. Fei, T. Montine, J. Quinn, 2021, bioRxiv.

Learning Gaussian graphical models with latent confounders

Alexander M. Franks, Sang-Yun Oh, Ke Wang, 2021, J. Multivar. Anal..

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

A. D'Amour, Alexander M. Franks, Jia-wen Zheng, 2021, 2102.09412.

Reducing subspace models for large‐scale covariance regression

Alexander M Franks, Alexander M. Franks, 2020, Biometrics.

Testing the adaptive value of sporulation in budding yeast using experimental evolution

Alexander M. Franks, S. Proulx, H. Teotónio, 2020, bioRxiv.

Post-transcriptional regulation across human tissues

E. Airoldi, Alexander M. Franks, N. Slavov, 2015, bioRxiv.