Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

doi:10.1016/j.chemosphere.2015.10.054

Cited by 33 publications

(14 citation statements)

References 19 publications

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“…Algorithm istkNN is a commercial tool [38] implementing a modified k-Nearest Neighbors (kNN) algorithm. kNN estimates the outcome of a sample in a dataset on the basis of read-across accounting for the k most similar samples (neighbors) in the TS for which the outcomes are known [39, 40]. If the algorithm is applied for predicting continuous endpoints (e.g., LD50 point estimates) the mean of the activities of neighbors is calculated [41].…”

Section: Methodsmentioning

confidence: 99%

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

et al. 2019

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The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

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Section: Methodsmentioning

confidence: 99%

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

et al. 2019

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“…For the P. promelas , the following algorithms were used to predict the toxicity of 64 OMPs: Acute Toxicity Read‐Across version 1.0.0 performed a read‐across on a dataset of 972 chemicals. The read‐across model was built with the istKNN application (developed by Kode) and is based on the similarity index . The SARpy (SAR in python) is based on an SAR (structure activity) model.…”

Section: Resultsmentioning

confidence: 99%

Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil

Silva

Lorenzo

Lopes

et al. 2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: The increase of chemical pollutants detected in different aquatic environments over the past few years has been in the focus of several studies related to their occurrence, transport, fate, and hazards, or risks to human and environmental health. In Brazil, recent studies have been conducted on the occurrence of a series of organic micropollutants (OMPs) in aquatic environments. Nevertheless, the toxicological information and environmental behavior for most of these pollutants are still difficult to evaluate. We obtained consensus models using a combina-tion of VolSurf and Dragon descriptors and Random Forest approach to predict the toxicity against Daphnia magna and Pimephales promelas for compounds identified in different sources of water in Brazil. The models showed satisfactory performance when compared with other approaches, such as Toxicity Estimation Software Tool (T.E.S.T.), etc. Both models can be used as complementary tools to aid in the prediction of organic compounds against both organisms.

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“…K-nearest neighbours algorithm is relatively easy in implementation and in analysis in comparison to other regression methods. KNN could be used in classification [16] or regression [17] problems. Modeling of failure rate of water pipes is based on regression not classification algorithm.…”

Section: K-nearest Neighbours Methodsmentioning

confidence: 99%

Application of K-nearest neighbours method for water pipes failure frequency assessment

Kutyłowska

2018

E3S Web Conf.

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The paper describes the results of failure rate modeling using K-nearest neighbours method (KNN). This algorithm is one among other regression methods, called machine learning methods. The aim of the presented paper was to check the possibilities of application of such kind of modelling and the comparison between current results and investigations of failure rate prediction in another Polish city. Operational data from 12 years of exploitation, received from water utility, were used to predict dependent variable (failure rate). Data (249 and 294 for distribution pipes and house connections, respectively) from the time span 2001–2012 were used for creating the KNN models. On the basis of other data (one case for each year) the validation of optimal model, based on Euclidean distance metric with the number of nearest neighbours K = 2, was carried out. The realization of the modelling was performed in the software program Statistica 12.0.

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Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm

Cited by 33 publications

References 19 publications

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil

Application of K-nearest neighbours method for water pipes failure frequency assessment

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