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@ARTICLE{Fonzino:300154,
      author       = {A. Fonzino and P. L. Mazzacuva and A. Handen and D. A.
                      Silvestris and A. Arnold$^*$ and R. Pecori$^*$ and G. Pesole
                      and E. Picardi},
      title        = {{REDI}net: a temporal convolutional network-based
                      classifier for {A}-to-{I} {RNA} editing detection harnessing
                      million known events.},
      journal      = {Briefings in bioinformatics},
      volume       = {26},
      number       = {2},
      issn         = {1467-5463},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-00640},
      pages        = {bbaf107},
      year         = {2025},
      abstract     = {A-to-I ribonucleic acid (RNA) editing detection is still a
                      challenging task. Current bioinformatics tools rely on
                      empirical filters and whole genome sequencing or whole exome
                      sequencing data to remove background noise, sequencing
                      errors, and artifacts. Sometimes they make use of cumbersome
                      and time-consuming computational procedures. Here, we
                      present REDInet, a temporal convolutional network-based deep
                      learning algorithm, to profile RNA editing in human RNA
                      sequencing (RNAseq) data. It has been trained on REDIportal
                      RNA editing sites, the largest collection of human A-to-I
                      changes from >8000 RNAseq data of the genotype-tissue
                      expression project. REDInet can classify editing events with
                      high accuracy harnessing RNAseq nucleotide frequencies of
                      101-base windows without the need for coupled genomic data.},
      keywords     = {Humans / RNA Editing / Computational Biology: methods /
                      Deep Learning / Algorithms / Neural Networks, Computer /
                      Sequence Analysis, RNA: methods / Software / A-to-I RNA
                      editing (Other) / REDItools (Other) / RNAseq (Other) /
                      temporal convolutional network (Other)},
      cin          = {D150},
      ddc          = {004},
      cid          = {I:(DE-He78)D150-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40112338},
      pmc          = {pmc:PMC11924403},
      doi          = {10.1093/bib/bbaf107},
      url          = {https://inrepo02.dkfz.de/record/300154},
}