Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Buontempo, Frances V.; Wang, Xue Zhong; Mwense, Mulaisho; Horan, N. J.; Young, Anita; Osborn, Daniel

doi:10.1021/ci049652n

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…Genetic programming is a member of the broad class of evolutionary algorithms that can efficiently search very large parameter spaces for locally optimal solutions to high dimensional materials spaces (Le and Winkler, 2016). The application of evolutionary algorithms for discovery and optimization of materials has been reviewed very recently (Le and Winkler, 2016 be successfully applied to modelling of ecotoxicity data (Buontempo et al, 2005). As the details of the technique can be found in literature (Wang et al, 2006, Buontempo et al, 2005, only a basic overview of the method is provided here.…”

Section: Methodsmentioning

confidence: 99%

“…The application of evolutionary algorithms for discovery and optimization of materials has been reviewed very recently (Le and Winkler, 2016 be successfully applied to modelling of ecotoxicity data (Buontempo et al, 2005). As the details of the technique can be found in literature (Wang et al, 2006, Buontempo et al, 2005, only a basic overview of the method is provided here.…”

Section: Methodsmentioning

confidence: 99%

“…We compiled nanotoxicity data from the literature, applied the GPTree method to model these data, and compared the results with past studies. Since the details of the method have been reported in recent literature (Ma et al, 2008, Wang et al, 2006, Buontempo et al, 2005, we provide only a summary. Here we demonstrate the successful application of a genetic programming-based decision tree construction algorithm to identify key physicochemical descriptors contributing to the toxicity of ENMs, and to automatically build easy-to-interpret decision tree models.…”

Section: Introductionmentioning

confidence: 99%

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Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

et al. 2016

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The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and ameliorate any risks to health or the environment posed by the presence of ENMs. Datadriven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity, and interpretation of the models they generate can be problematic. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large data sets, are not particularly good at feature selection, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets.We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with additional advantages that it works with small datasets, automatically selects descriptors, and provides improved interpretability of models.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

et al. 2016

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“…the physicochemical descriptors that contribute to the observed toxicity) and to remove the descriptors that are not related to the endpoint of interest. In a previous study, (Buontempo, et al, 2005) demonstrated the use of a genetic programming-based decision tree generation technique for in silico toxicity prediction. They developed a decision tree model, involving five descriptors selected from a pool of more than a thousand descriptors, that has good predictive performance for both training and test datasets.…”

Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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“…An insight into the potential benefits of using optimization‐based prediction models for the toxicity of real‐valued data is provided in Buontempo, Wang, Mwense, and Horan (). In this case, GP is used in the induction of decision trees for application to two eco‐toxicity datasets of organic compounds, both with a large number of inputs and four classes obtained from equal frequency splitting of the endpoint.…”

Section: Related Workmentioning

confidence: 99%

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

2019

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Cheminformatics plays a vital role in maintaining large amounts of chemical data. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State‐of‐the‐art prediction methods such as neural networks and multilayer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a quantum‐inspired genetic programming model to improve prediction accuracy. Genetic programming is utilized to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best‐of‐run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the quantum‐inspired genetic programming model has better goodness‐of‐fit statistics then and significantly outperforms the neural network model.

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Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data

Cited by 19 publications

References 40 publications

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

(Q)SAR modelling of nanomaterial toxicity: A critical review

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

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