Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Gravitation field algorithm and its application in gene cluster. | Zheng et al. Algorithms for Molecular Biology 2010 5 32 http content 5 1 32 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Gravitation field algorithm and its application in gene cluster Ming Zheng1 Gui-xia Liu Chun-guang Zhou Yan-chun Liang Yan Wangt Abstract Background Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA GA PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. Results This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm GFA which is derived from the famous astronomy theory Solar Nebular Disk Model SNDM of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. Conclusions The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code in Matlab are publicly available at http . CSBG GFA. Background Two of the most challenging tasks of optimization algorithms are to search the global optimum and to find all local optima of the space of solutions in clustering genes from available experimental data 1 . the gene expression profiles or given functions. In view of recent technological developments for large-scale measurements of DNA expression level these two problems can often be formulated and many methods have been proposed. In particular the heuristic searches are more promising .
