http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A new multi-class classification method based on minimum enclosing balls
QingJun Song,XingMing Xiao,HaiYan Jiang,XieGuang Zhao 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.8
With respect to classification problems, the Minimum enclosing ball (MEB) method was recently studied by some scholars as a newsupport vector machine. As a nascent technology, however, MEB reports poor adaptability for different types of samples, especiallymulti-class samples. In this paper, we propose a new multi-class classification method based on MEB. This method is derived from eachclass sample center and radius with the Gaussian kernel width factor parameter σ, which is labelled as σ-MEB. σ is a variable parameteraccording to the different sample characteristics. When this parameter is considered, the multi-class classifier is easy to adapt and is robustin diverse datasets. The quadratic programming problem was transformed into its dual form with Lagrange multipliers using thismethod. Finally, we applied sequential minimal optimization method and Karush—Kuhn—Tucker conditions to accelerate the trainingprocess. Numerical experiment results indicate that given different types of samples, the proposed method is more accurate than themethods with which it is compared. Moreover, the proposed method reports values in the upper quantile with respect to adaptation performance.
Identifying Responsive Functional Modules from Protein-Protein Interaction Network
Zikai Wu,Xingming Zhao,Luonan Chen 한국분자세포생물학회 2009 Molecules and cells Vol.27 No.3
Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.