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Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
VK. Ojha, K. Jackowski, A. Abraham, V. Snášel,
Language English Country New Zealand
Document type Journal Article, Research Support, Non-U.S. Gov't
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PubMed
25709436
DOI
10.2147/ijn.s71847
Knihovny.cz E-resources
- MeSH
- Algorithms MeSH
- Lactic Acid chemistry MeSH
- Polyglycolic Acid chemistry MeSH
- Microspheres MeSH
- Nanoparticles chemistry MeSH
- Solubility MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
Department of Computer Science VŠB Technical University of Ostrava Ostrava Czech Republic
Department of Systems and Computer Networks Wrocław University of Technology Wrocław Poland
IT4Innovations VŠB Technical University of Ostrava Ostrava Czech Republic
References provided by Crossref.org
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