Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092529" target="_blank" >RIV/61989100:27240/14:86092529 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092529

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921864" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2014.6921864</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921864" target="_blank" >10.1109/NaBIC.2014.6921864</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile

Popis výsledku v původním jazyce

Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factorsleads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble methodproposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniq

Název v anglickém jazyce

Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile

Popis výsledku anglicky

Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factorsleads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble methodproposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniq

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

NaBIC 2014 ; CASoN 2014 : July 30-31, Porto, Portugal

ISBN

978-1-4799-5937-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

121-126

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Porto

Datum konání akce

30. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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