tailieunhanh - Báo cáo sinh học: "Speeding up the Consensus Clustering methodology for microarray data analysis"

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: Speeding up the Consensus Clustering methodology for microarray data analysis. | Giancarlo and Utro Algorithms for Molecular Biology 2011 6 1 http content 6 1 1 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Speeding up the Consensus Clustering methodology for microarray data analysis Raffaele Giancarlo1 Filippo Utro2 Abstract Background The inference of the number of clusters in a dataset a fundamental problem in Statistics Data Analysis and Classification is usually addressed via internal validation measures. The stated problem is quite difficult in particular for microarrays since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset . functionally related genes. Despite the rich literature present in that area the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap we propose a speed-up of Consensus Consensus Clustering a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset together with a dissimilarity matrix the consensus matrix that can be used by clustering algorithms. As detailed in the remainder of the paper Consensus is a natural candidate for a speed-up. Results Since the time-precision performance of Consensus depends on two parameters our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is the closely related algorithm FC Fast Consensus that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications such as cancer studies gene expression with up and down patterns and a full spectrum of dimensionality up to over a thousand. Based on their outcome compared with previous benchmarking results .

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