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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
Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample // International journal of molecular sciences, 12 (2011), 12; 8415-8430 doi:10.3390/ijms12128415 (međunarodna recenzija, članak, znanstveni)


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

Autori
Radman, Andreja ; Gredičak, Matija ; Kopriva, Ivica ; Jerić, Ivanka

Izvornik
International journal of molecular sciences (1422-0067) 12 (2011), 12; 8415-8430

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Opioid growth factor (OGF); QSAR descriptors; consensus of predictors

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Kemija



POVEZANOST RADA


Projekt / tema
098-0982903-2558 - Analiza višespektralih podataka (Ivica Kopriva, )
098-0982933-2936 - Kemijske preobrazbe prirodnih spojeva (Lidija Varga-Defterdarović, )

Ustanove
Institut "Ruđer Bošković", Zagreb

Citiraj ovu publikaciju

Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka
Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample // International journal of molecular sciences, 12 (2011), 12; 8415-8430 doi:10.3390/ijms12128415 (međunarodna recenzija, članak, znanstveni)
Radman, A., Gredičak, M., Kopriva, I. & Jerić, I. (2011) Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample. International journal of molecular sciences, 12 (12), 8415-8430 doi:10.3390/ijms12128415.
@article{article, year = {2011}, pages = {8415-8430}, DOI = {10.3390/ijms12128415}, keywords = {opioid growth factor (OGF), QSAR descriptors, consensus of predictors}, journal = {International journal of molecular sciences}, doi = {10.3390/ijms12128415}, volume = {12}, number = {12}, issn = {1422-0067}, title = {Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample}, keyword = {opioid growth factor (OGF), QSAR descriptors, consensus of predictors} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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