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Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample

Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka (2011) Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample. International Journal of Molecular Sciences, 12 (12). pp. 8415-8430. ISSN 1422-0067

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Abstract

Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k- nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF ; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.

Item Type: Article
Uncontrolled Keywords: opioid growth factor (OGF), QSAR descriptors, consensus of predictors
Subjects: NATURAL SCIENCES > Mathematics > Applied Mathematics and Mathematical Modeling
NATURAL SCIENCES > Chemistry > Organic Chemistry
NATURAL SCIENCES > Biology
Divisions: Division of Laser and Atomic Research and Development
Division of Organic Chemistry and Biochemistry
Projects:
Project titleProject leaderProject codeProject type
Analiza višespektralih podataka[217905] Ivica Kopriva098-0982903-2558MZOS
Kavezasti spojevi: ugradbene jedinice u molekularnim sustavima[27342] Kata Majerski098-0982933-2911MZOS
Depositing User: Ivica Kopriva
Date Deposited: 29 Apr 2015 13:56
Last Modified: 29 Apr 2015 13:56
URI: http://fulir.irb.hr/id/eprint/1811
DOI: 10.3390/ijms12128415

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