Conformal prediction of biological activity of chemical compounds. / Toccaceli, Paolo; Nouretdinov, Ilia; Gammerman, Alexander.

In: Annals of Mathematics and Artificial Intelligence, Vol. 81, No. 1-2, 10.2017, p. 105–123.

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Conformal prediction of biological activity of chemical compounds. / Toccaceli, Paolo; Nouretdinov, Ilia; Gammerman, Alexander.

In: Annals of Mathematics and Artificial Intelligence, Vol. 81, No. 1-2, 10.2017, p. 105–123.

Research output: Contribution to journalArticle

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Toccaceli, Paolo ; Nouretdinov, Ilia ; Gammerman, Alexander. / Conformal prediction of biological activity of chemical compounds. In: Annals of Mathematics and Artificial Intelligence. 2017 ; Vol. 81, No. 1-2. pp. 105–123.

BibTeX

@article{bc7db4c6e2c645e3935ed1853fcf3b5e,
title = "Conformal prediction of biological activity of chemical compounds",
abstract = "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), highdimensionality 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.",
keywords = "Conformal Prediction, Confidence Estimation, Chemoinformatics, Non-Conformity Measure",
author = "Paolo Toccaceli and Ilia Nouretdinov and Alexander Gammerman",
year = "2017",
month = "10",
doi = "10.1007/s10472-017-9556-8",
language = "English",
volume = "81",
pages = "105–123",
journal = "Annals of Mathematics and Artificial Intelligence",
issn = "1012-2443",
publisher = "Springer Netherlands",
number = "1-2",

}

RIS

TY - JOUR

T1 - Conformal prediction of biological activity of chemical compounds

AU - Toccaceli, Paolo

AU - Nouretdinov, Ilia

AU - Gammerman, Alexander

PY - 2017/10

Y1 - 2017/10

N2 - 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), highdimensionality 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.

AB - 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), highdimensionality 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.

KW - Conformal Prediction

KW - Confidence Estimation

KW - Chemoinformatics

KW - Non-Conformity Measure

U2 - 10.1007/s10472-017-9556-8

DO - 10.1007/s10472-017-9556-8

M3 - Article

VL - 81

SP - 105

EP - 123

JO - Annals of Mathematics and Artificial Intelligence

JF - Annals of Mathematics and Artificial Intelligence

SN - 1012-2443

IS - 1-2

ER -