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• W3160500979 abstract "treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations." @default.
• W3160500979 created "2021-05-24" @default.
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• W3160500979 date "2021-05-17" @default.
• W3160500979 modified "2023-10-14" @default.
• W3160500979 title "treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses" @default.
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• W3160500979 doi "https://doi.org/10.1186/s13059-021-02368-1" @default.
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