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Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.
- Source :
-
Talanta . Jan2016, Vol. 147, p609-614. 6p. - Publication Year :
- 2016
-
Abstract
- Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00399140
- Volume :
- 147
- Database :
- Academic Search Index
- Journal :
- Talanta
- Publication Type :
- Academic Journal
- Accession number :
- 111096366
- Full Text :
- https://doi.org/10.1016/j.talanta.2015.10.047