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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86095763" target="_blank" >RIV/61989100:27240/15:86095763 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86095763 RIV/61989100:27730/15:86095763

Výsledek na webu

<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4327564/" target="_blank" >http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4327564/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2147/IJN.S71847" target="_blank" >10.2147/IJN.S71847</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 regressionalgorithms 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 o

Název v anglickém jazyce

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

Popis výsledku anglicky

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 regressionalgorithms 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 o

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Nanomedicine

ISSN

1178-2013

e-ISSN

—

Svazek periodika

2015

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

NZ - Nový Zéland

Počet stran výsledku

1129

Strana od-do

1119

Kód UT WoS článku

000348985600001

EID výsledku v databázi Scopus

2-s2.0-84922363645