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• W140019785 abstract "ABSTRACT This paper describes that actomyosin complex particles are automatically selected. We propose a new approach, which combines both gray level co-occurrence matrix to extract texture features and SVM classifier to detect actomyosin complex particles automatically. Experimental results show that detection rate achieves 93.58%, the false positive rate is 3.66%, and the area under the ROC curve (AUC) is 0.9645. KEYWORDS Gray level co-occurrence matrix, support vector machine, actomyosin complex 1. INTRODUCTION Myosin is to exist in muscle and non-muscle tissue. About 50% of the protein in a muscle cell is myosin, about 30% of myosin are bound to action (Molecular motors, 2002). Myosin is the best studies molecular motor. Due to one want to understand how myosin produces force, it is necessary to visualize the structure of myosin. Information on the myosin bound to actin can be obtained using Cryo-EM. Investigators can see that the views of large scale conformational changes in the actomyosin comparison of 3D reconstruction. The information contributed to the understanding the force production and its function. EOS (Extensible and Object-oriented System) is a group of small tools include three-dimensional reconstruction of macromolecules (Takuo Y. et al, 1996). The single particle analysis has been widely used for 3D reconstruction of large molecular complexes from Cryo-EM image. Owing to the low signal to noise ratio in Cryo-EM images, one will require a hundreds of thousands or even million of high resolution particles, which make it impractical to manually pick the particles. The current main method is: Template-matching, edge detection, intensity comparison, texture analysis, neural network, and feature-based approach, etc. For classification algorithm, several machine learning algorithms have been used to classify protein particle datasets, in which include k-nearest neighbor, decision trees, Fisher linear discriminant analysis, Bayesian networks, neural networks, and SVM. For particle analysis, the particle selection is critical and become a bottleneck in high the resolution structure determination of macromolecules using Cryo-EM. This is an unresolved challenging problem. This demands development of fast and accurate detection algorithm (Yuanxin Z. et al, 2004). Such as Yongyi Yang et al. proposed SVM approach for detection for microcalcifications (Yongyi Yang, et al, 2002); Zeyun Yun et al. proposed feature extraction from the edge map (Zeyun Y, et al, 2004); Roseman, A.M., proposed particle finding using a fast local correlation algorithm (Roseman A. et al, 2003), and Zhu,Y. et al. proposed fast detection of generic biological particles (Zhu Y. et al, 2002). These algorithms can achieve over 90% detecting rate (Furey T., et al, 2000 and XU., et al, 2003), the false positive rate ranging from 15- 30%, and the lowest false positive rate is 4.5% (Yuanxin Z. et al, 2004). Our detecting rate achieved 93.58%, and the false positive rate is 3.66%. SVM can handle large feature space, can effectively avoid over fitting by controlling the margin, and can automatically identify a small subset. SVM is being increasingly used for solving biological problems, including colonography detection (Anna K. et al, 2003), protein analysis (Y.F.Sun, et al, 2003), and genetic" @default.
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• W140019785 date "2005-01-01" @default.
• W140019785 modified "2023-09-23" @default.
• W140019785 title "AUTOMATIC ACTOMYOSIN COMPLEX SELECTION USING SVM" @default.
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