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Search results for: Urska Cvek

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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>Search results for: Urska Cvek</title> <meta name="description" content="Search results for: Urska Cvek"> <meta name="keywords" content="Urska Cvek"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science 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Cvek</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Multidimensional Visualization Tools for Analysis of Expression Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Urska%20Cvek">Urska Cvek</a>, <a href="https://publications.waset.org/search?q=Marjan%20Trutschl"> Marjan Trutschl</a>, <a href="https://publications.waset.org/search?q=Randolph%20Stone%20II"> Randolph Stone II</a>, <a href="https://publications.waset.org/search?q=Zanobia%20Syed"> Zanobia Syed</a>, <a href="https://publications.waset.org/search?q=John%20L.%20Clifford"> John L. Clifford</a>, <a href="https://publications.waset.org/search?q=Anita%20L.%20Sabichi"> Anita L. Sabichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Expression data analysis is based mostly on the statistical approaches that are indispensable for the study of biological systems. Large amounts of multidimensional data resulting from the high-throughput technologies are not completely served by biostatistical techniques and are usually complemented with visual, knowledge discovery and other computational tools. In many cases, in biological systems we only speculate on the processes that are causing the changes, and it is the visual explorative analysis of data during which a hypothesis is formed. We would like to show the usability of multidimensional visualization tools and promote their use in life sciences. We survey and show some of the multidimensional visualization tools in the process of data exploration, such as parallel coordinates and radviz and we extend them by combining them with the self-organizing map algorithm. We use a time course data set of transitional cell carcinoma of the bladder in our examples. Analysis of data with these tools has the potential to uncover additional relationships and non-trivial structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=microarrays" title="microarrays">microarrays</a>, <a href="https://publications.waset.org/search?q=visualization" title=" visualization"> visualization</a>, <a href="https://publications.waset.org/search?q=parallel%20coordinates" title=" parallel coordinates"> parallel coordinates</a>, <a href="https://publications.waset.org/search?q=radviz" title="radviz">radviz</a>, <a href="https://publications.waset.org/search?q=self-organizing%20maps." title=" self-organizing maps."> self-organizing maps.</a> </p> <a href="https://publications.waset.org/10119/multidimensional-visualization-tools-for-analysis-of-expression-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10119/apa" target="_blank" rel="nofollow" class="btn btn-primary 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