E
Edoardo M. Airoldi
Researcher at Temple University
Publications - 230
Citations - 20370
Edoardo M. Airoldi is an academic researcher from Temple University. The author has contributed to research in topics: Estimator & Inference. The author has an hindex of 50, co-authored 224 publications receiving 18276 citations. Previous affiliations of Edoardo M. Airoldi include Google & Harvard University.
Papers
More filters
Journal ArticleDOI
Stochastic blockmodels with a growing number of classes.
TL;DR: It is shown that the fraction of misclassified network nodes converges in probability to zero under maximum likelihood fitting when the number of classes is allowed to grow as the root of the network size and the average network degree grows at least poly-logarithmically in this size.
Journal ArticleDOI
Differential Stoichiometry among Core Ribosomal Proteins
Nikolai Slavov,Nikolai Slavov,Stefan Semrau,Edoardo M. Airoldi,Bogdan Budnik,Alexander van Oudenaarden +5 more
TL;DR: The existence of ribosomes with distinct protein composition and physiological function is supported by using mass spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast.
Proceedings Article
Mixed Membership Stochastic Blockmodels
TL;DR: This paper describes a latent variable model of such data called the mixed membership stochastic blockmodel, which extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation.
Journal ArticleDOI
Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast
TL;DR: Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels, substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
Posted Content
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
TL;DR: In this article, a stochastic block model approximation (SBA) of the graphon is proposed to estimate a graphon from a set of observed networks generated from the graph.