Conformal prediction of biological activity of chemical compounds
| Autor: | Ilia Nouretdinov, Alexander Gammerman, Paolo Toccaceli |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
business.industry Applied Mathematics Feature vector Conformal map 02 engineering and technology Mondrian Machine learning computer.software_genre Domain (software engineering) Set (abstract data type) 03 medical and health sciences 030104 developmental biology Ranking Artificial Intelligence Biological target Cheminformatics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Mathematics |
| Zdroj: | Annals of Mathematics and Artificial Intelligence |
| ISSN: | 1012-2443 |
| DOI: | 10.1007/s10472-017-9556-8 |
| Popis: | The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order. |
| Databáze: | OpenAIRE |
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