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

TL;DR

This paper introduces a new statistical method to quantify expert opinions on chemical compound clusters in high-dimensional data, addressing computational challenges and demonstrating improved performance over traditional maximum likelihood approaches.

Contribution

A novel permutational-splitting sample procedure for hierarchical models that efficiently handles high-dimensional data in expert opinion quantification.

Findings

Method compares favorably with maximum likelihood in simulations.

Reliable procedure effectively manages high-dimensional fixed effects.

Case study illustrates practical application of the approach.

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.