guest ::
| Home > Publications database > REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events. > print |
| Home > Publications database > REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events. > print |
| 001 | 300154 | ||
| 005 | 20250328112422.0 | ||
| 024 | 7 | _ | |a 10.1093/bib/bbaf107 |2 doi |
| 024 | 7 | _ | |a pmid:40112338 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC11924403 |2 pmc |
| 024 | 7 | _ | |a 1467-5463 |2 ISSN |
| 024 | 7 | _ | |a 1477-4054 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2025-00640 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Fonzino, Adriano |b 0 |
| 245 | _ | _ | |a REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events. |
| 260 | _ | _ | |a Oxford [u.a.] |c 2025 |b Oxford University Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1743149945_31629 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 314 - Immunologie und Krebs (POF4-314) |0 G:(DE-HGF)POF4-314 |c POF4-314 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a A-to-I RNA editing |2 Other |
| 650 | _ | 7 | |a REDItools |2 Other |
| 650 | _ | 7 | |a RNAseq |2 Other |
| 650 | _ | 7 | |a temporal convolutional network |2 Other |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a RNA Editing |2 MeSH |
| 650 | _ | 2 | |a Computational Biology: methods |2 MeSH |
| 650 | _ | 2 | |a Deep Learning |2 MeSH |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Neural Networks, Computer |2 MeSH |
| 650 | _ | 2 | |a Sequence Analysis, RNA: methods |2 MeSH |
| 650 | _ | 2 | |a Software |2 MeSH |
| 700 | 1 | _ | |a Mazzacuva, Pietro Luca |b 1 |
| 700 | 1 | _ | |a Handen, Adam |b 2 |
| 700 | 1 | _ | |a Silvestris, Domenico Alessandro |b 3 |
| 700 | 1 | _ | |a Arnold, Annette |0 P:(DE-He78)7c776439971ef21f36ac730cfbff7fff |b 4 |u dkfz |
| 700 | 1 | _ | |a Pecori, Riccardo |0 P:(DE-He78)a8b399fa71eacddc353846ca1d9d2127 |b 5 |u dkfz |
| 700 | 1 | _ | |a Pesole, Graziano |0 0000-0003-3663-0859 |b 6 |
| 700 | 1 | _ | |a Picardi, Ernesto |0 0000-0002-6549-0114 |b 7 |
| 773 | _ | _ | |a 10.1093/bib/bbaf107 |g Vol. 26, no. 2, p. bbaf107 |0 PERI:(DE-600)2036055-1 |n 2 |p bbaf107 |t Briefings in bioinformatics |v 26 |y 2025 |x 1467-5463 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:300154 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 4 |6 P:(DE-He78)7c776439971ef21f36ac730cfbff7fff |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-He78)a8b399fa71eacddc353846ca1d9d2127 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-314 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Immunologie und Krebs |x 0 |
| 914 | 1 | _ | |y 2025 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2024-12-28 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2024-12-28 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2024-12-28 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2024-12-28 |
| 920 | 1 | _ | |0 I:(DE-He78)D150-20160331 |k D150 |l Immundiversität |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)D150-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|