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Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Authors Ojha VK, Jackowski K, Abraham A, Snášel V

Received 28 July 2014

Accepted for publication 16 October 2014

Published 4 February 2015 Volume 2015:10(1) Pages 1119—1129

DOI https://doi.org/10.2147/IJN.S71847

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Thomas J Webster

Varun Kumar Ojha,1,2 Konrad Jackowski,3 Ajith Abraham,1,4 Václav Snášel1,2

1IT4Innovations, VŠB – Technical University of Ostrava, Ostrava, Czech Republic; 2Department of Computer Science, VŠB – Technical University of Ostrava, Ostrava, Czech Republic; 3Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland; 4Machine Intelligence Research Labs, Auburn, WA, USA


Abstract: Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.

Keywords: feature selection, regression models, ensemble, protein dissolution

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