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A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

, , и . Bioinform., 32 (12): 111-120 (2016)

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A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps., , и . Bioinform., 32 (12): 111-120 (2016)Input Validation for Semi-supervised Clustering., , и . ICDM Workshops, стр. 479-483. IEEE Computer Society, (2006)On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering., , and . ICDE, page 329-340. IEEE Computer Society, (2005)Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data., , , , , , and . Bioinform., 30 (8): 1049-1055 (2014)Identifying Projected Clusters from Gene Expression Profiles., , , and . BIBE, page 259-266. IEEE Computer Society, (2004)A semi-supervised approach to projected clustering with applications to microarray data., , , , and . Int. J. Data Min. Bioinform., 3 (3): 229-259 (2009)On Mining Micro-array data by Order-Preserving Submatrix., , , , and . ICDE Workshops, page 1153. IEEE Computer Society, (2005)OMTools: a software package for visualizing and processing optical mapping data., , , and . Bioinform., 33 (18): 2933-2935 (2017)Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses., , , , and . Cell, 143 (4): 639--650 (12.11.2010)Efficient Clustering of Uncertain Data., , , , , and . ICDM, page 436-445. IEEE Computer Society, (2006)