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Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules.

, , and . Bioinform., 25 (12): 1521-1527 (2009)

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Temporal clustering of gene expression patterns using short-time segments., , , , , , and . I. J. Functional Informatics and Personalised Medicine, 4 (1): 32-46 (2012)Computational prediction of microRNA regulatory pathways., , , and . GENSiPS, page 158-161. IEEE, (2011)Improving performance of mammalian microRNA target prediction., , , , and . BMC Bioinform., (2010)Deep learning tackles single-cell analysis - a survey of deep learning for scRNA-seq analysis., , , , , , , , , and 3 other author(s). Briefings Bioinform., (2022)Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods., , , , , , and . PLoS Comput. Biol., (2019)Temporal clustering of gene expression patterns using short-time segments., , , , , , and . BIBM Workshops, page 173-178. IEEE, (2010)MeT-DB V2.0: elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome., , , , , , , , , and 1 other author(s). Nucleic Acids Res., 46 (Database-Issue): D281-D287 (2018)Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules., , and . Bioinform., 25 (12): 1521-1527 (2009)Computational prediction of MicroRNA regulatory pathways., , , and . BIBM Workshops, page 386-391. IEEE, (2010)A Bayesian Approach for Identifying miRNA Targets by Combining Sequence Prediction and Expression Profiling., , , and . IJCBS, page 185-189. IEEE Computer Society, (2009)