tailieunhanh - Báo cáo sinh học: "A markov classification model for metabolic pathways"

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: A markov classification model for metabolic pathways. | Hancock and Mamitsuka Algorithms for Molecular Biology 2010 5 10 http content 5 1 10 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access A markov classification model for metabolic pathways Timothy Hancock Hiroshi Mamitsuka Abstract Background This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model HME3M first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response. Results We compared the performance of HME3M with logistic regression and support vector machines SVM for both simulated pathways and on two metabolic networks glycolysis and the pentose phosphate pathway for Arabidopsis thaliana. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of Arabidopsis. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of Arabidopsis. Conclusions This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data. Background Networks are a natural way of understanding complex processes involving interactions between many variables. Visualizing a process as a network allows the researcher to form an intuitive understanding of complex phenomena. A clear example of the effective use of networks is the visualization of metabolic networks to provide a detailed map of key chemical reactions and their genetic .