Ridge regression ensemble for toxicity prediction
Authors: Budka, M. and Gabrys, B.
Journal: Procedia Computer Science
Volume: 1
Issue: 1
Pages: 193-201
eISSN: 1877-0509
DOI: 10.1016/j.procs.2010.04.022
Abstract:Traditional methods of assessing chemical toxicity of various compounds require tests on animals, which raises ethical concerns and is expensive. Current legislation may lead to a further increase of demand for laboratory animals in the next years. As a result, automatically generated predictions using Quantitative Structure-Activity Relationship (QSAR) modelling approaches appear as an attractive alternative. Due to sparsity of the chemical space, making this kind of predictions is however a difficult task. In this paper we propose a purely data-driven, rigorous and universal methodology of QSAR modelling, based on ensemble of relatively simple ridge regressors trained in various subspaces of the chemical space, selected using an iterative optimization procedure. The model described has been developed without using any domain knowledge and has been evaluated within the Environmental Toxicity Prediction Challenge CADASTER 2009, which has attracted over 100 participants from 25 countries. The presented approach was chosen as one of the First-Pass Winners, with predictive power non-significantly different to the highest ranked method, developed by the experts in the area of QSAR modelling and toxicology.
Source: Scopus
Ridge regression ensemble for toxicity prediction
Authors: Budka, M. and Gabrys, B.
Journal: ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS
Volume: 1
Issue: 1
Pages: 193-201
ISSN: 1877-0509
DOI: 10.1016/j.procs.2010.04.022
Source: Web of Science (Lite)
Ridge regression ensemble for toxicity prediction
Authors: Budka, M. and Gabrys, B.
Journal: Procedia Computer Science
Volume: 1
Pages: 193-201
ISSN: 1877-0509
DOI: 10.1016/j.procs.2010.04.022
Abstract:Traditional methods of assessing chemical toxicity of various compounds require tests on animals, which raises ethical concerns and is expensive. Current legislation may lead to a further increase of demand for laboratory animals in the next years. As a result, automatically generated predictions using Quantitative Structure–Activity Relationship (QSAR) modelling approaches appear as an attractive alternative. Due to sparsity of the chemical space, making this kind of predictions is however a difficult task.
In this paper we propose a purely data–driven, rigorous and universal methodology of QSAR modelling, based on ensemble of relatively simple ridge regressors trained in various subspaces of the chemical space, selected using an iterative optimization procedure. The model described has been developed without using any domain knowledge and has been evaluated within the Environmental Toxicity Prediction Challenge CADASTER 2009, which has attracted over 100 participants from 25 countries. The presented approach was chosen as one of the First–Pass Winners, with predictive power non–significantly different to the highest ranked method, developed by the experts in the area of QSAR modelling and toxicology.
Source: Manual
Preferred by: Marcin Budka
Ridge regression ensemble for toxicity prediction.
Authors: Budka, M. and Gabrys, B.
Editors: Sloot, P.M.A., Albada, G.D.V. and Dongarra, J.J.
Journal: ICCS
Volume: 1
Pages: 193-201
Publisher: Elsevier
DOI: 10.1016/j.procs.2010.04.022
https://www.sciencedirect.com/science/journal/18770509/1/1
Source: DBLP