Edoardo M Airoldi
Edoardo M Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
確認したメール アドレス: fas.harvard.edu
Statistical network analysis with the igraph software package
G Csárdi, T Nepusz, EM Airoldi
New York, NY: Springer, 2016
Guidelines for the use and interpretation of assays for monitoring autophagy
DJ Klionsky, K Abdelmohsen, A Abe, MJ Abedin, H Abeliovich, ...
Autophagy 12 (1), 1-222, 2016
Mixed membership stochastic blockmodels
EM Airoldi, DM Blei, SE Fienberg, EP Xing
Journal of Machine Learning Research 9 (3), 1981-2014, 2008
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Y Katz, ET Wang, EM Airoldi, CB Burge
Nature methods 7 (12), 1009-1015, 2010
A survey of statistical network models
A Goldenberg, AX Zheng, SE Fienberg, EM Airoldi
Foundations and Trends in Machine Learning 2 (2), 129-233, 2010
The structural topic model and applied social science
ME Roberts, BM Stewart, D Tingley, EM Airoldi
NIPS Workshop on Topic Models: Computation, Application, and Evaluation 4, 1-20, 2013
Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast
MJ Brauer, C Huttenhower, EM Airoldi, R Rosenstein, JC Matese, ...
Molecular biology of the cell 19 (1), 352-367, 2008
A model of text for experimentation in the social sciences
ME Roberts, BM Stewart, EM Airoldi
Journal of the American Statistical Association 111 (515), 988-1003, 2016
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
Systems-level dynamic analyses of fate change in murine embryonic stem cells
R Lu, F Markowetz, RD Unwin, JT Leek, EM Airoldi, BD MacArthur, ...
Nature 462 (7271), 358-362, 2009
Stochastic blockmodels with a growing number of classes
DS Choi, PJ Wolfe, EM Airoldi
Biometrika 99 (2), 273-284, 2012
Mixed membership stochastic block models for relational data with application to protein-protein interactions
EM Airoldi, DM Blei, SE Fienberg, EP Xing
ENAR International Biometrics Society Annual Meeting, 2006
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
EM Airoldi, TB Costa, SH Chan
Advances in Neural Information Processing Systems (NIPS), 2013
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
Differential stoichiometry among core ribosomal proteins
N Slavov, S Semrau, EM Airoldi, B Budnik, A van Oudenaarden
Cell Reports, 2015
Quantitative visualization of alternative exon expression from RNA-seq data
Y Katz, ET Wang, J Silterra, S Schwartz, B Wong, H Thorvaldsdóttir, ...
Bioinformatics 31 (14), 2400-2402, 2015
Predicting cellular growth from gene expression signatures
EM Airoldi, C Huttenhower, D Gresham, C Lu, AA Caudy, MJ Dunham, ...
PLoS Computational Biology 5 (1), e1000257, 2009
Integrating utility into face de-identification
R Gross, E Airoldi, B Malin, L Sweeney
International Workshop on Privacy Enhancing Technologies (PET), 227-242, 2005
Summarizing topical content with word frequency and exclusivity
J Bischof, EM Airoldi
Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012
Defining the essential function of yeast Hsf1 reveals a compact transcriptional program for maintaining eukaryotic proteostasis
EJ Solís, JP Pandey, X Zheng, DX Jin, PB Gupta, EM Airoldi, D Pincus, ...
Molecular Cell, 2016
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