0000000000749993

AUTHOR

Andreas Maunz

showing 2 related works from this author

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties.

2016

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine str…

0301 basic medicineQuantitative structure–activity relationshipread acrossPredictive Clustering Tree (PCT) methodComputer science610010501 environmental sciencescomputer.software_genre600 Technik Medizin angewandte Wissenschaften::610 Medizin und Gesundheit01 natural sciences03 medical and health sciencesPharmacology (medical)Cluster analysis0105 earth and related environmental sciencesOriginal ResearchAlternative methodsPharmacologytoxicological and structural similaritybusiness.industryQSARlcsh:RM1-950non-animal methods; QSAR; readacross; Predictive Clustering Tree (PCT) method; toxicological and structural similarityIdentification (information)Tree (data structure)030104 developmental biologyConceptual approachlcsh:Therapeutics. PharmacologyKnowledge basenon-animal methodsData miningWeb servicebusinesscomputerFrontiers in pharmacology
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Extracting information from support vector machines for pattern-based classification

2014

Statistical machine learning algorithms building on patterns found by pattern mining algorithms have to cope with large solution sets and thus the high dimensionality of the feature space. Vice versa, pattern mining algorithms are frequently applied to irrelevant instances, thus causing noise in the output. Solution sets of pattern mining algorithms also typically grow with increasing input datasets. The paper proposes an approach to overcome these limitations. The approach extracts information from trained support vector machines, in particular their support vectors and their relevance according to their coefficients. It uses the support vectors along with their coefficients as input to pa…

business.industryComputer scienceFeature vectorSolution setPattern recognitioncomputer.software_genreGraphDomain (software engineering)Support vector machineRelevance (information retrieval)Fraction (mathematics)Noise (video)Artificial intelligenceData miningbusinesscomputerProceedings of the 29th Annual ACM Symposium on Applied Computing
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