Thu, 07 Aug 2008 21:25:19 BST CiteULike: AbnerCYH's Zou http://www.citeulike.org/user/AbnerCYH/author/Zou CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/AbnerCYH/article/465479 <i>Journal of Biomedical Informatics, Vol. 38, No. 5. (October 2005), pp. 404-415.</i><br /><br />Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a model's ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software. The use of receiver operating characteristic curves in biomedical informatics Thomas Lasko Jui Bhagwat Kelly Zou Lucila Ohno-Machado doi:10.1016/j.jbi.2005.02.008 Journal of Biomedical Informatics, Vol. 38, No. 5. (October 2005), pp. 404-415. 2006-01-14T16:08:59-00:00 2005 Journal of Biomedical Informatics 38 5 404 415 biology complex information kdd math stochastic http://www.citeulike.org/user/AbnerCYH/article/2696796 <i>Vol. 4947 (2008)</i><br /><br />Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20,22,4,23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times. Summarization Graph Indexing: Beyond Frequent Structure-Based Approach Lei Zou Lei Chen Huaming Zhang Yansheng Lu Qiang Lou Vol. 4947 (2008) 2008-04-21T14:40:28-00:00 2008 4947 algorithms data_structure graph