title: Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.
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.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China; School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Yang
authors.affiliation: Key Laboratory of Luminescence and Real-Time Analytical Chemistry, Ministry of Education, College of Pharmaceutical Science, Southwest University, Chongqing 400715, China.
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Zou
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Li-Juan
authors.initials: LJ
authors.lastname: Tang
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Guo-Li
authors.initials: GL
authors.lastname: Shen
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Jiang
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Ru-Qin
authors.initials: RQ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D008856:Microscopy, Fluorescence; D021381:Protein Transport; D060388:Support Vector Machine
keywords: Non-linear machine learning; Pattern unmixing; Protein distribution; Support vector machine; Variable weight