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The potential use of micronucleus assays in plants for the detection of genotoxic effects of heavy-metal ions was investigated. Three different plant systems were comparatively investigated in micronucleus tests with Tradescantia pollen mother cells (Trad MCN) and micronucleus tests with meristematic root tip cells of Allium cepa and Vicia faba (Allium/(More)
This paper explores the utility of data mining and machine learning algorithms for the induction of mutagenicity structure-activity relationships (SARs) from noncongeneric data sets. We compare (i) a newly developed algorithm (MOLFEA) for the generation of descriptors (molecular fragments) for noncongeneric compounds with traditional SAR approaches(More)
Motivation: The Predictive Toxicology Challenge (PTC) was initiated to stimulate the development of advanced techniques for predictive toxicology models. The goal of this challenge was to compare different approaches for the prediction of rodent carcinogenicity, based on the experimental results of the US National Toxicology Program (NTP). Results: 111 sets(More)
The single-cell gel electrophoresis (or comet) assay has gained widespread acceptance as a cheap and simple genotoxicity test, but it requires a computer-assisted image-analysis system. As commercial programs are expensive and inflexible, we decided to develop an image-analysis system based on public domain programs and make it publicly available for the(More)
lazar is a new tool for the prediction of toxic properties of chemical structures. It derives predictions for query structures from a database with experimentally determined toxicity data. lazar generates predictions by searching the database for compounds that are similar with respect to a given toxic activity and calculating the prediction from their(More)
Human Hep G2 cells have retained the activities of phase I and phase II enzymes which are involved in the metabolism of environmental genotoxins. The present study describes the results of single cell gel electrophoresis (SCGE) assays with a panel of different model compounds with these cells. With genotoxic carcinogens such as aflatoxin B(1) (AFB(1)),(More)
Intestinal drug absorption in humans is a central topic in drug discovery. In this study, we use a broad selection of machine learning and statistical methods for the classification and numerical prediction of this key end point. Our data set is based on a selection of 458 small druglike compounds with FDA approval. Using easily available tools, we(More)
We initiated the Predictive Toxicology Challenge (PTC) to stimulate the development of advanced SAR techniques for predictive toxicology models. The goal of this challenge is to predict the rodent carcinogenicity of new compounds based on the experimental results of the US National Toxicology Program (NTP). Submissions will be evaluated on quantitative and(More)
It is a well-known fact that propositional learning algorithms require \good" features to perform well in practice. So a major step in data engineering for inductive learning is the construction of good features by domain experts. These features often represent properties of structured objects, where a property typically is the occurrence of a certain(More)
MOTIVATION The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. RESULTS Fourteen machine(More)