A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data

E. Milanzi, A. Alonso, C. Buyck, G. Molenberghs, L. Bijnens

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. We propose a method to quantify these qualitative assessments using hierarchical models. However, with the most commonly available computing resources, the high dimensionality of the vectors of fixed effects and correlated responses renders maximum likelihood unfeasible in this scenario. We devise a reliable procedure to tackle this problem and show, using theoretical arguments and simulations, that the new methodology compares favorably with maximum likelihood, when the latter option is available. The approach was motivated by a case study, which we present and analyze.

Original languageEnglish
Pages (from-to)2319-2335
Number of pages17
JournalAnnals of Applied Statistics
Volume8
Issue number4
DOIs
Publication statusPublished - Dec 2014

Keywords

  • Maximum likelihood
  • pseudo-likelihood
  • rater
  • split samples
  • REGRESSION
  • REDUCTION
  • MODELS

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