2 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures
Emilie Eliseussen, Haakon Muggerud, Luca Coraggio +3
With the increasing availability of ranking data, there has been a growing demand for appropriate unsupervised rank-based inferential frameworks capable of handling high-dimensiona…
Rank-based Bayesian clustering via covariate-informed Mallows mixtures
Emilie Eliseussen, Arnoldo Frigessi, Valeria Vitelli
Data in the form of rankings, ratings, pair comparisons or clicks are frequently collected in diverse fields, from marketing to politics, to understand assessors' individual prefer…
Rank-based Bayesian variable selection for genome-wide transcriptomic analyses
Emilie Eliseussen, Thomas Fleischer, Valeria Vitelli
Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data st…