Statistical evaluation of the Predictive Toxicology
Challenge 2000–2001

Toivonen, Hannu; Srinivasan, Ashwin; King, Ross D.; Krämer, Stefan; Helma, Christoph

doi:10.1093/bioinformatics/btg130

Cited by 163 publications

(82 citation statements)

References 12 publications

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“…Fourteen groups submitted 111 models, but only five were better than random at a significance level of p=0.05, with accuracies of predictions between 25 and 79% (Toivonen et al, 2003). Two previous comparative exercises by the NTP had challenged models with 44 and 30 chemicals prospectively, i.e., with chemicals which were to be tested only (Benigni and Giuliani, 2003).…”

Section: Integrated Testing Strategies (Its)mentioning

confidence: 99%

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

Basketter

Clewell

Kimber

et al. 2012

ALTEX

203

165

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Section: Integrated Testing Strategies (Its)mentioning

confidence: 99%

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

Basketter

Clewell

Kimber

et al. 2012

ALTEX

203

165

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“…The Predictive Toxicology Challenge (Toivonen et al, 2003) data is a dataset of molecules dealing with Table 3. Classification results (in percents) for the MU-TAG dataset using second order random walk Markov model together with Morgan index description of molecules.…”

Section: Ptc Data Setmentioning

confidence: 99%

Extensions of marginalized graph kernels

Mahé

Ueda

Akutsu

et al. 2004

Twenty-First International Conference on Machine Learning - ICML '04

134

110

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Positive definite kernels between labeled graphs have recently been proposed. They enable the application of kernel methods, such as support vector machines, to the analysis and classification of graphs, for example, chemical compounds. These graph kernels are obtained by marginalizing a kernel between paths with respect to a random walk model on the graph vertices along the edges. We propose two extensions of these graph kernels, with the double goal to reduce their computation time and increase their relevance as measure of similarity between graphs. First, we propose to modify the label of each vertex by automatically adding information about its environment with the use of the Morgan algorithm. Second, we suggest a modification of the random walk model to prevent the walk from coming back to a vertex that was just visited. These extensions are then tested on benchmark experiments of chemical compounds classification, with promising results.

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“…The dataset used in this experiment is taken from the Predictive Toxicity Challenge [34] which aims to predict carcinogenicity of chemical compounds applied to four type of animals : female (F) and male (M) rats (R) and mice (M). This experiment consists in ten different datasets for each class of animal, each of them being composed of one training set and one test set.…”

Section: Predictive Toxicity Challengementioning

confidence: 99%

Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics

Gaüzère

Grenier

Brun

et al. 2015

Pattern Recognition

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Chemoinformatics is a research field concerned with the study of physical or biological molecular properties through computer science's research fields such as machine learning and graph theory. From this point of view, graph kernels provide a nice framework which allows to naturally combine machine learning and graph theory techniques. Graph kernels based on bags of patterns have proven their efficiency on several problems both in terms of accuracy and computational time. Treelet kernel is a graph kernel based on a bag of small subtrees. We propose in this paper several extensions of this kernel devoted to chemoinformatics problems. These extensions aim to weight each pattern according to its influence, to include the comparison of non-isomorphic patterns, to include stereo information and finally to explicitly encode cyclic information into kernel computation.

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Statistical evaluation of the Predictive Toxicology Challenge 2000–2001

Abstract: PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.

Cited by 163 publications

References 12 publications

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

Extensions of marginalized graph kernels

Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics

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