Using Venn-Abers predictors to assess cardio-vascular risk

Ernst Ahlberg, Ruben Buendia, Lars Carlsson
Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 91:132-146, 2018.

Abstract

This study investigates a method for predicting compound risk based on in vitro assay data and estimated $C_\textitmax$, the maximum concentration of a drug in the body. The method makes use of Venn-Abers predictors and Support Vector Machines to compute compound risk with respect to a biological target. The method has been applied to in vitro ion-channel data generated to assess cardiac risk and introduces a more intuitive way to reflect cardiac risk.

Cite this Paper

BibTeX
@InProceedings{pmlr-v91-ahlberg18a, title = {Using {V}enn-{Abers} predictors to assess cardio-vascular risk}, author = {Ahlberg, Ernst and Buendia, Ruben and Carlsson, Lars}, booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {132--146}, year = {2018}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Peeters, Ralf}, volume = {91}, series = {Proceedings of Machine Learning Research}, month = {11--13 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v91/ahlberg18a/ahlberg18a.pdf}, url = {https://proceedings.mlr.press/v91/ahlberg18a.html}, abstract = {This study investigates a method for predicting compound risk based on in vitro assay data and estimated $C_\textitmax$, the maximum concentration of a drug in the body. The method makes use of Venn-Abers predictors and Support Vector Machines to compute compound risk with respect to a biological target. The method has been applied to in vitro ion-channel data generated to assess cardiac risk and introduces a more intuitive way to reflect cardiac risk.} }
Endnote
%0 Conference Paper %T Using Venn-Abers predictors to assess cardio-vascular risk %A Ernst Ahlberg %A Ruben Buendia %A Lars Carlsson %B Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2018 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Ralf Peeters %F pmlr-v91-ahlberg18a %I PMLR %P 132--146 %U https://proceedings.mlr.press/v91/ahlberg18a.html %V 91 %X This study investigates a method for predicting compound risk based on in vitro assay data and estimated $C_\textitmax$, the maximum concentration of a drug in the body. The method makes use of Venn-Abers predictors and Support Vector Machines to compute compound risk with respect to a biological target. The method has been applied to in vitro ion-channel data generated to assess cardiac risk and introduces a more intuitive way to reflect cardiac risk.
APA
Ahlberg, E., Buendia, R. & Carlsson, L.. (2018). Using Venn-Abers predictors to assess cardio-vascular risk. Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 91:132-146 Available from https://proceedings.mlr.press/v91/ahlberg18a.html.

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