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: Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps. | Algorithms for Molecular Biology BioMed Central Open Access Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps Peter Meinicke 1 Thomas Lingner1 Alexander Kaever1 Kirstin Feussner2 Cornelia Gobel3 Ivo Feussner3 Petr Karlovsky4 and Burkhard Morgenstern1 Address Department of Bioinformatics Institute of Microbiology and Genetics University of Gottingen Gottingen Germany 2Department of Developmental Biochemistry Institute for Biochemistry and Molecular Cell Biology University of Gottingen Gottingen Germany 3Department for Plant Biochemistry Albrecht-von-Haller-Institute for Plant Sciences University of Gottingen Gottingen Germany and 4Molecular Phytopathology and Mycotoxin Research Unit University of Gottingen Gottingen Germany Email Peter Meinicke - pmeinic@ Thomas Lingner - thomas@ Alexander Kaever - alex@ Kirstin Feussner - kfeussn@ Cornelia Gobel - cgoebel@ Ivo Feussner - ifeussn@ Petr Karlovsky - pkarlov@ Burkhard Morgenstern - burkhard@ Corresponding author Published 26 June 2008 Received 24 January 2008 Algorithms for Molecular Biology 2008 3 9 doi 1748-7188-3-9 Accepted 26 June 2008 This article is available from http content 3 1 9 2008 Meinicke et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative .
