Abstract

Get full access to this article
View all access options for this article.
References
1.
Vo
AH
,
Van Vleet
TR
,
Gupta
RR
, et al. An overview of machine learning and big data for drug toxicity evaluation . Chem Res Toxicol 2020 ;33(1):20–37; doi: 10.1021/acs.chemrestox.9b00227
2.
Lidbury
BA.
A new in vitro toxicology: Shifting from cells to serum by exploiting pathology data and machine learning to investigate liver toxicity . Appl In Vitro Toxicol 2016 ;2(4):217–222.
3.Eskes C (ed.). Applied In Vitro Toxicology Special Issue on Integrated Strategy Approaches in Toxicology . 2021 ;7(3):89–155.
4.
Wang
MWH
,
Goodman
JM
,
Allen
TEH
. Machine learning in predictive toxicology: recent applications and future directions for classification models. Chem Res Toxicol 2021 ;34(2):217–239; doi: 10.1021/acs.chemrestox.0c00316
5.
Tang
W
,
Chen
J
,
Wang
Z
, et al. Deep learning for predicting toxicity of chemicals: A mini review . J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 2018 ;36(4):252–271; doi: 10.1080/10590501.2018.1537563
6.
Leist
M
,
Lidbury
BA
,
Yang
C
, et al. Novel technologies and an overall strategy to allow hazard assessment and risk prediction of chemicals, cosmetics, and drugs with animal-free methods . ALTEX 2012 ;29(4):373–388.
7.
Kingsford
C
,
Salzberg
SL
. What are decision trees? Nat Biotechnol 2008 ;26(9):1011–1013.
8.
Therneau
T
,
Atkinson
B
. rpart:
Recursive Partitioning and Regression
Trees
. R package version 4.1-15. 2019 . Available from: https://CRAN.R-project.org/package=rpart [Last accessed: November 30, 2022].
9.
Breiman
L.
Random forests . Machine Learning 2001 ;45(1):5–32.
10.
Cortes
C
,
Vapnik
V
. Support-vector networks.
Machine
Learning
. 1995 ;20(3):273–297.
11.
Caragea
C
,
Sinapov
J
,
Silvescu
A
, et al. Glycosylation site prediction using ensembles of Support Vector Machine classifiers . BMC Bioinformatics 2007 ;8(1):1–3.
12.
Karatzoglou
A
,
Meyer
D
,
Hornik
K.
Support Vector Machines in R . J Stat Softw 2006 ;15(9).
