Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals
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In: Environmental toxicology and chemistry / SETAC, Vol. 32, No. 5, 05.2013, p. 1187-1195.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals
AU - Strempel, Sebastian
AU - Nendza, Monika
AU - Scheringer, Martin
AU - Hungerbühler, Konrad
N1 - Copyright © 2013 SETAC.
PY - 2013/5
Y1 - 2013/5
N2 - The present study presents a data-oriented, tiered approach to assessing the bioaccumulation potential of chemicals according to the European chemicals regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). The authors compiled data for eight physicochemical descriptors (partition coefficients, degradation half-lives, polarity, and so forth) for a set of 713 organic chemicals for which experimental values of the bioconcentration factor (BCF) are available. The authors employed supervised machine learning methods (conditional inference trees and random forests) to derive relationships between the physicochemical descriptors and the BCF values. In a first tier, the authors established rules for classifying a chemical as bioaccumulative (B) or nonbioaccumulative (non-B). In a second tier, the authors developed a new tool for estimating numerical BCF values. For both cases the optimal set of relevant descriptors was determined; these are biotransformation half-life and octanol-water distribution coefficient (log D) for the classification rules and log D, biotransformation half-life, and topological polar surface area for the BCF estimation tool. The uncertainty of the BCF estimates obtained with the new estimation tool was quantified by comparing the estimated and experimental BCF values of the 713 chemicals. Comparison with existing BCF estimation methods indicates that the performance of this new BCF estimation tool is at least as high as that of existing methods. The authors recommend the present study's classification rules and BCF estimation tool for a consensus application in combination with existing BCF estimation methods.
AB - The present study presents a data-oriented, tiered approach to assessing the bioaccumulation potential of chemicals according to the European chemicals regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). The authors compiled data for eight physicochemical descriptors (partition coefficients, degradation half-lives, polarity, and so forth) for a set of 713 organic chemicals for which experimental values of the bioconcentration factor (BCF) are available. The authors employed supervised machine learning methods (conditional inference trees and random forests) to derive relationships between the physicochemical descriptors and the BCF values. In a first tier, the authors established rules for classifying a chemical as bioaccumulative (B) or nonbioaccumulative (non-B). In a second tier, the authors developed a new tool for estimating numerical BCF values. For both cases the optimal set of relevant descriptors was determined; these are biotransformation half-life and octanol-water distribution coefficient (log D) for the classification rules and log D, biotransformation half-life, and topological polar surface area for the BCF estimation tool. The uncertainty of the BCF estimates obtained with the new estimation tool was quantified by comparing the estimated and experimental BCF values of the 713 chemicals. Comparison with existing BCF estimation methods indicates that the performance of this new BCF estimation tool is at least as high as that of existing methods. The authors recommend the present study's classification rules and BCF estimation tool for a consensus application in combination with existing BCF estimation methods.
KW - Chemistry
KW - Biotransformation
KW - Environmental Monitoring
KW - Environmental Pollutants
KW - Environmental Pollution
KW - Half-Life
KW - Organic Chemicals
KW - Trees
UR - http://www.scopus.com/inward/record.url?scp=84876417882&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/8a2d8b7d-fd00-39e5-b703-9cb2adca3cd6/
U2 - 10.1002/etc.2150
DO - 10.1002/etc.2150
M3 - Journal articles
C2 - 23382013
VL - 32
SP - 1187
EP - 1195
JO - Environmental toxicology and chemistry / SETAC
JF - Environmental toxicology and chemistry / SETAC
SN - 1552-8618
IS - 5
ER -