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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18648

Title: A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data
Authors: Milanzi, Elasma
Alonso Abad, Ariel
Buyck, Christophe
Molenberghs, Geert
Bijnens, Luc
Issue Date: 2014
Citation: Annals of Applied Statistics, 8 (4), p. 2319-2335
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.
Notes: E-mail Addresses:elasma.milanzi@uhasselt.be; ariel.alonso@maastrichtuniversity.nl; cbuyck@its.jnj.com; geert.molenberghs@uhasselt.be; lbijnens@its.jnj.com
URI: http://hdl.handle.net/1942/18648
Link to publication: https://arxiv.org/pdf/1502.00754.pdf
DOI: 10.1214/14-AOAS772
ISI #: 000347530200020
ISSN: 1932-6157
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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