Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
Jazyk angličtina Země Nový Zéland Médium electronic-ecollection
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
25709436
PubMed Central
PMC4327564
DOI
10.2147/ijn.s71847
PII: ijn-10-1119
Knihovny.cz E-zdroje
- Klíčová slova
- ensemble, feature selection, protein dissolution, regression models,
- MeSH
- algoritmy MeSH
- kopolymer kyseliny glykolové a mléčné MeSH
- kyselina mléčná chemie MeSH
- kyselina polyglykolová chemie MeSH
- mikrosféry MeSH
- nanočástice chemie MeSH
- rozpustnost MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- kopolymer kyseliny glykolové a mléčné MeSH
- kyselina mléčná MeSH
- kyselina polyglykolová 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.
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