First Application of a Distance-Based Outlier Approach to Detect Highly Differentiated Genomic Regions Across Human Populations
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-23497-7_10
Reference33 articles.
1. 1000 Genomes Project Consortium, Abecasis, G.R., Auton, A., Brooks, L.D., DePristo, M.A., Durbin, R.M., Handsaker, R.E., Kang, H.M., Marth, G.T., McVean, G.A.: An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422), 56–65 (2012)
2. Angiulli, F., Basta, S., Lodi, S., Sartori, C.: Distributed strategies for mining outliers in large data sets. IEEE Trans. Knowl. Data Eng. 25(7), 1520–1532 (2013)
3. Angiulli, F., Basta, S., Lodi, S., Sartori, C.: Fast outlier detection using a gpu. In: International Conference on High Performance Computing and Simulation (HPCS), pp. 143–150 (2013)
4. Angiulli, F., Pizzuti, C.: Outlier mining in large high-dimensional data sets. Trans. Knowl. Data Eng. 2(17), 203–215 (2005)
5. Angiulli, F., Basta, S., Lodi, S., Sartori, C.: Accelerating outlier detection with intra- and inter-node parallelism. In: International Conference on High Performance Computing and Simulation (HPCS), pp. 476–483. IEEE, Bologna, Italy, 21–25 July (2014)
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