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REDInet: a TCN-based classifier for A-to-I RNA editing detection harnessing million known events

2024-09-10

Abstract excerpt

<title>Abstract</title> <p>A-to-I RNA editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and WGS/WES data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. We present here REDInet, a TCN-based Deep Learning algorithm, to profile RNA editing in human RNAseq data. It has...

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Literature Corpus work
853afceb-00a4-5d7d-aaba-7d37e18cd8a0
DOI
10.21203/rs.3.rs-4900829/v1
Open publication

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REDInet: a TCN-based classifier for A-to-I RNA editing detection harnessing million known eventsDOI 10.21203/rs.3.rs-4900829/v1
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