Paper Info
Title
Dimensionality Reduction and Prediction of the Protein Macromolecule Dissolution Profile.
Abstract
A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro- and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account, we have a dataset in hand with three hundred input features. Therefore, a primary approach before identifying a regression model is to reduce the dimensionality of the dataset at hand. On the one hand, we have adopted Backward Elimination Feature selection techniques for an exhaustive analysis of the predictability of each combination of features. On the other hand, several linear and non-linear feature extraction methods are used in order to extract a new set of features out of the available dataset. A comprehensive experimental analysis for the selection or extraction of features and identification of corresponding prediction model is offered. The designed experiment and prediction models offers substantially better performance over the earlier proposed prediction models in literature for the said problem.
Year
DOI
Venue
2014
10.1007/978-3-319-08156-4_30
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014)
Keywords
Field
DocType
Dimension reduction,Feature selection,Feature extraction,Regression,PLGA
Predictability,Dimensionality reduction,Feature selection,Regression,Pattern recognition,Regression analysis,Feature extraction,Curse of dimensionality,Artificial intelligence,Engineering,Machine learning,Design of experiments
Conference
Volume
ISSN
Citations 
303
2194-5357
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Varun Kumar Ojha1329.25
Konrad Jackowski210.72
Václav Snasel31261210.53
Ajith Abraham48954729.23