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Multiple-Instance Case-Based Learning for Predictive Toxicology
[chapter]
2004
Lecture Notes in Computer Science
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We present a new approach to lazy learning, based on the notion of multiple-instance, which is capable of seamlessly working with multiple descriptions. ...
Machine Learning (ML) in general, and lazy learning techniques in particular, have been applied to the task of predictive toxicology. ...
A Predictive Toxicology Challenge (PTC) [15] was held in 2001 focusing on machine learning techniques for predicting the toxicity of compounds. ...
doi:10.1007/978-3-540-30478-4_18
fatcat:uhaxodtopra27ons6n4b4lvxde
An Effective Combination Based on Class-Wise Expertise of Diverse Classifiers for Predictive Toxicology Data Mining
[chapter]
2006
Lecture Notes in Computer Science
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The classification methods used to generate classifiers for combination are chosen in terms of their representability and diversity and include the Instance-based Learning algorithm (IBL), Decision Tree ...
This paper presents a study on the combination of different classifiers for toxicity prediction. Two combination operators for the Multiple-Classifier System definition are also proposed. ...
The parameter LR for MLP in Table 3 stands for learning rate; the parameter k for IBL stands for the number of nearest neighbours used for classifying new instances and the parameter C for SVM stands ...
doi:10.1007/11811305_18
fatcat:3zwecjy67baw7gg72z4drx25v4
A Data-Driven Approach for Improved Effective Classification in Predictive Toxicology
2006
2006 IEEE International Conference on Computational Cybernetics
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The paper proposes a correlative data-oriented fusion algorithm to develop effective models based on multisource data for classification applied to predictive toxicology. ...
Prediction of toxic effects of chemical compounds based on experiments involving animals and human beings is very expensive in terms of time, social and financial cost. ...
Predictive Toxicology models are based on restricted numbers of experimental data, for which generalization is still challenging. ...
doi:10.1109/icccyb.2006.305708
fatcat:i32rvd6cl5gizom3vg6tbdtqwy
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
2008
BMC Bioinformatics
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Conclusion: We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology ...
Filter-based feature selection generally improved performance, most strikingly for LDA. ...
EPA's National Center for Computational Toxicology and approved for publication. ...
doi:10.1186/1471-2105-9-241
pmid:18489778
pmcid:PMC2409339
fatcat:o7yqmd66afhsnpn5k3yfs76vuu
Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning
2020
International Journal of Molecular Sciences
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This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models. ...
Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. ...
[50] built models based on perturbation theory using data from multiple literature sources to predict aggregated toxicological endpoints in a binary form for different NPs. ...
doi:10.3390/ijms21155280
pmid:32722414
fatcat:qqbht3m75vachoklz4xs2x5l7a
Perpest model, a case-based reasoning approach to predict ecological risks of pesticides
2002
Environmental Toxicology and Chemistry
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This model is based on case-based reasoning, a technique that solves new problems (e.g., what is the effect of pesticide A?) by using past experience (e.g., published microcosm experiments). ...
This allows the model to predict effects of pesticides for which no effects on a semifield scale have been published. ...
The PERPEST model is available; e-mail the first author for more information. ...
doi:10.1002/etc.5620211132
pmid:12389932
fatcat:n7wlhuhbjbh7bccljjdbwfzuoy
PERPEST MODEL, A CASE-BASED REASONING APPROACH TO PREDICT ECOLOGICAL RISKS OF PESTICIDES
2002
Environmental Toxicology and Chemistry
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This model is based on case-based reasoning, a technique that solves new problems (e.g., what is the effect of pesticide A?) by using past experience (e.g., published microcosm experiments). ...
This allows the model to predict effects of pesticides for which no effects on a semifield scale have been published. ...
The PERPEST model is available; e-mail the first author for more information. ...
doi:10.1897/1551-5028(2002)021<2500:pmacbr>2.0.co;2
pmid:12389932
fatcat:pte76p4zdvhbbh36htd465paiy
Collaborative development of predictive toxicology applications
2010
Journal of Cheminformatics
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: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset ...
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. ...
Acknowledgements OpenTox OpenTox -An Open Source Predictive Toxicology Framework, http://www. opentox.org, is funded under the EU Seventh Framework Program: HEALTH- ...
doi:10.1186/1758-2946-2-7
pmid:20807436
pmcid:PMC2941473
fatcat:pzvkgrzm5jhk7b7zqcztauxbqe
The State-of-the Art of Environmental Toxicogenomics: Challenges and Perspectives of "Omics" Approaches Directed to Toxicant Mixtures
2019
International Journal of Environmental Research and Public Health
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These examples illustrate the importance of exploring multiple "omes" and the purpose of "omics" and multi-"omics" for building truly predictive models of hazard and risk. ...
Despite costs and demanding computations, the systems toxicology framework, of which "omics" is a major component, endeavors extracting adverse outcome pathways for complex mixtures. ...
As before, important lessons can be learned from ecotoxicologists. For instance, Song et al. ...
doi:10.3390/ijerph16234718
pmid:31779274
pmcid:PMC6926496
fatcat:4vzjevw64jcqnj4safypkispoa
Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases
[chapter]
2009
Lecture Notes in Computer Science
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In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors. ...
Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. ...
A classification system TIPT (Tree Induction for Predictive Toxicology) based on the tree was then applied and compared with neural networks models in terms of accuracy and understandability. ...
doi:10.1007/978-3-642-03223-3_11
fatcat:ca6duj4zqjcdngw7bta6ag2kxy
Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data
2012
Toxicological Sciences
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Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded ...
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection ...
For example, in the simplest instance, predictions from a QSAR model and a biological model would be averaged into a final consensus score. ...
doi:10.1093/toxsci/kfs095
pmid:22387746
pmcid:PMC3327873
fatcat:jib5s7h3gngyxlxo2ovicjpkve
Big-data and machine learning to revamp computational toxicology and its use in risk assessment
2018
Toxicology Research
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The creation of large toxicological databases and advances in machine-learning techniques have empowered computational approaches in toxicology. ...
Multi-label learning Recent advances in machine learning have resulted in models that can handle missing data and model multiple targets at once (multi-label learning, in case of toxicology for example ...
This decision tree illustrates how instance-based learning can be used in concert with supervised learning methods via feature generation. ...
doi:10.1039/c8tx00051d
pmid:30310652
pmcid:PMC6116175
fatcat:ms7njv5sbnh6dfbv5lhospcowi
Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties
2000
Environmental Health Perspectives
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It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). ...
Articles Every technique for toxicity prediction and for the detection of structure-activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. ...
For this reason we decided to remove toxicologically irrelevant entities (e.g., multiple instances of the same entity, H 2 O, HCl, etc.), represent salts with covalent bonds, and remove compounds with ...
doi:10.1289/ehp.001081029
pmid:11102292
pmcid:PMC1240158
fatcat:ogvb7hui5vb53nxg7g664hyyxm
Data Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Properties
2000
Environmental Health Perspectives
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It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). ...
Articles Every technique for toxicity prediction and for the detection of structure-activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. ...
For this reason we decided to remove toxicologically irrelevant entities (e.g., multiple instances of the same entity, H 2 O, HCl, etc.), represent salts with covalent bonds, and remove compounds with ...
doi:10.2307/3434954
fatcat:cz5tdnekdvcrfhkct4yy3dxbsy
Cross-organism toxicogenomics with group factor analysis
2014
Systems Biomedicine
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This is a key step toward developing methods for predictive toxicology. ...
The associations can also be used for predicting one data view based on another, for example, predicting toxic outcomes based on transcriptomic responses. ...
doi:10.4161/sysb.29291
fatcat:qkdfrtrbvbdytnjtto5xc2n76y
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