Development of Nearest Neighbor Classifiers Identifying Dermal Sensitizers Based on a Local Lymph Node Assay Database
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2007/07/29
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Description:K Nearest Neighbor classifiers were developed to predict skin sensitization of a new chemical based on a murine local lymph node assay database of 178 organic chemicals. Two filters were compared for preselection of molecular descriptors. The Fisher's Discriminant Ratio filter picked a subset of descriptors which turn out to be more discriminatory than those picked by the t-test filter. Then, a step forward search method was implemented to screen out extra descriptors and simplify the classifiers based on leave one-out accuracy. Euclidean and Mahalanobis distance metrics were also examined and the results showed the Mahalanobis distance was appropriate for this study. The 3-nearest neighbor classifier of 13 descriptors singled out by the above methods has an especially balanced performance with sensitivity of 92% and specificity of 81 % for this unbalanced dataset. [Description provided by NIOSH]
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NIOSHTIC Number:nn:20033174
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Citation:Statistics: Harnessing the Power of Information. 2007 JSM Proceedings. Papers Presented at the Joint Statistical Meetings Salt Lake City, Utah, July 29 - August 2, 2007, and other ASA-sponsored conferences. Alexandria, VA: American Statistical Association, 2007 Jul; :CD-ROM
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Federal Fiscal Year:2007
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Peer Reviewed:False
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Source Full Name:Statistics: Harnessing the Power of Information. 2007 JSM Proceedings. Papers Presented at the Joint Statistical Meetings Salt Lake City, Utah, July 29 - August 2, 2007, and other ASA-sponsored conferences
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End Date:20050929
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Main Document Checksum:urn:sha-512:6527d5aec997b3f823be2df604f21ad9ce61851e93b7af7b9ea201f3b046e19dc734e068ffa0530372105579edc37a31b9f1772c351aca8e50214dc7170bc87c
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