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Edoardo M. Airoldi

Temple University · US
Area of research
Statistics and Probability · Statistical and Nonlinear Physics
Research interest
Research interests include Complex Network Analysis Techniques, Advanced Causal Inference Techniques, Bioinformatics and Genomic Networks, and Bayesian Methods and Mixture Models.
h-index
44
citations
16,158
works
230
NIH funding
primary concept
email

Recent publications

Gut microbiome strain-sharing within isolated village social networks
Nature 2024cited by 40position: middledoi
Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis
Molecular Cell 2018cited by 148position: middledoi
Post-transcriptional regulation across human tissues
PLoS Computational Biology 2017cited by 236position: middledoi
Asymptotic and finite-sample properties of estimators based on stochastic gradients
The Annals of Statistics 2017cited by 107position: lastdoi
Detecting Network Effects
2017cited by 63position: lastdoi
Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
Autophagy 2016cited by 5,978position: middledoi
A Model of Text for Experimentation in the Social Sciences
Journal of the American Statistical Association 2016cited by 720position: lastdoi
Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis
Molecular Cell 2016cited by 190position: middledoi
Steady-state and dynamic gene expression programs in<i>Saccharomyces cerevisiae</i>in response to variation in environmental nitrogen
Molecular Biology of the Cell 2016cited by 43position: firstdoi
A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution
eLife 2016cited by 32position: middledoi
Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress
Cell 2015cited by 506position: middledoi
Quantitative visualization of alternative exon expression from RNA-seq data
Bioinformatics 2015cited by 240position: middledoi
Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast
PLoS Genetics 2015cited by 200position: middledoi
A natural experiment of social network formation and dynamics
Proceedings of the National Academy of Sciences 2015cited by 132position: lastdoi
Estimating cellular pathways from an ensemble of heterogeneous data sources
2015cited by 3position: last
Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis
Cell Reports 2014cited by 113position: middledoi
Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state
eLife 2014cited by 92position: middledoi
Sashimi plots: Quantitative visualization of alternative isoform expression from RNA-seq data
bioRxiv (Cold Spring Harbor Laboratory) 2014cited by 7position: middledoi
Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state
bioRxiv (Cold Spring Harbor Laboratory) 2014cited by 2position: middledoi
Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data
Molecular Biology and Evolution 2013cited by 56position: middledoi
Stochastic blockmodels with a growing number of classes
Biometrika 2012cited by 238position: lastdoi

Grants

III: Medium: Design and analysis of experiments on networked populations
NSF1941159$564,6392018–2021PIRePORTER
CAREER: Quantifying diffusion and dynamics on healthcare, innovation and communication networks
NSF1937978$104,8532018–2020PIRePORTER
III: Medium: Design and analysis of experiments on networked populations
NSF1409177$1,120,8202014–2019PIRePORTER
16th Meeting of New Researchers in Statistics and Probability, July 31- August 2, 2014
NSF1418827$20,0002014–2014PIRePORTER
CAREER: Quantifying diffusion and dynamics on healthcare, innovation and communication networks
NSF1149662$495,2082012–2019PIRePORTER
Collaborative proposal: Statistical methods for analyzing complexity and growth of large biological and information networks
NSF1106980$75,0002011–2014PIRePORTER
III: Small: Representation, Modeling and Inference for Large Biological and Information Networks
NSF1017967$513,7802010–2014PIRePORTER
Collaborative Research: Models for Network Evolution: A Study of Growth and Structure in the Wikipedia
NSF0907009$59,4852009–2011PIRePORTER

Frequent collaborators

Piyush B. Gupta · Massachusetts Institute of Technology4 papers (2014–2018)Christopher B. Burge · IIT@MIT4 papers (2014–2015)Yarden Katz · Massachusetts Institute of Technology4 papers (2014–2015)Alexander Franks · University of California, Santa Barbara4 papers (2015–2017)D. Allan Drummond · University of Chicago3 papers (2013–2015)Edward W. Wallace · Wellcome Centre for Cell Biology2 papers (2013–2015) · 2 papers (2014–2015)Bang Wong · Massachusetts Institute of Technology2 papers (2014–2015)Nikolai Slavov · Massachusetts Institute of Technology2 papers (2014–2017)Ethan S. Sokol · Foundation Medicine (United States)2 papers (2014–2014) · 2 papers (2016–2018)Zhengquan Yu · Liaoning University of Traditional Chinese Medicine2 papers (2014–2014)Albert W. Cheng · Massachusetts Institute of Technology2 papers (2014–2014)Jill P. Mesirov · University of California San Diego2 papers (2014–2015)Wai Leong Tam · Agency for Science, Technology and Research2 papers (2014–2014)Rudolf Jaenisch · Massachusetts Institute of Technology2 papers (2014–2014)Feifei Li · Hebei Medical University2 papers (2014–2014) · 2 papers (2014–2015)Christopher J. Lengner · University of Alabama at Birmingham2 papers (2014–2014)Eric T. Wang · Allen Institute for Brain Science2 papers (2014–2015)