HOME- Bryn Mawr Conference
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- Bassan, A
- Cronin, M
- Hardy, B
- Helma, C
- Hopfinger, T
- Judson, P
- Leahy, D
- Madden, J
- Michielan, L
- Narayanan, D
- Myatt, G
- Obrezanova, O
- Thomas, S
- Zamora, I
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Dr. Christoph Helma is a computational toxicologist with more than 10 years experience in the development and application of advanced data mining techniques for toxicological applications, predominantly for the identification of Structure-Activity Relationships. He is the developer of the award winning lazar system for the prediction of toxic activities (www.predictive-toxicology.org/lazar/), has been an invited keynote speaker at major scientific conferences and has published more than 30 peer-reviewed articles. C.H. was the main organizer of the Predictive Toxicology Challenge 2000-2001, editor for a special section in Bioinformatics, and editor of a book about Predictive Toxicology. He is the founder and head of "in silico toxicology", a spin-off company of the University Freiburg, Germany. He is presently the bioinformatics workpackage leader for the Sens-it-iv EU project and works on the implementation of a inductive database for the interactive analysis of toxicogenomics, -proteomics and -metabonomics data. He was awarded with the Research Prize for Alternative Methods to Animal Experiments (German Federal Ministry on Consumer Protection, Food and Agriculture, 2005) and the Research Prize for Cancer Research without Animal Experiments (Doctors Against Animal Experiments, 2006).
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Lazy-Structure-Activity-Relationships (lazar) for the in silico Prediction of Chemical Carcinogenicity
Christoph Helma (in silico toxicology)
In this workshop the group will apply the lazar (Lazy Structure Activity Relationships) system for the prediction of biological activities and its application for the prediction of carcinogenicity, an endpoint that is very hard to predict with existing (Q)SAR techniques.
lazar uses a modified k-nearest-neighbor algorithm, that is capable of detecting activity specific chemical similarities, to derive predictions for untested structures from a database with experimental toxicities. lazar relies on relatively few model assumptions and provides the rationales for predictions in an understandable and traceable manner. The system is capable of discriminating reliably between trustworthy and untrustworthy predictions (e.g. for structures that fall beyond the scope of the training set) by assigning a confidence index to each prediction.
The group will carry out cross-validation experiments with various carcinogenicity and mutagenicity endpoints to determine the predictive accuracies for structures within the applicability domain of the training data. The group will determine where lazar can reliably identify cases where the information in the database is insufficient and/or contradictory to derive valid predictions.
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