5' and 3' UTR translational regulatory features: ML pipeline and trained models

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: Ariel Ogran, Joseph Green, Daniel Hayat, Sansrity Sinha, Ori Avinoam, Ido Azuri, Rivka Dikstein
DOI: 10.5281/zenodo.18657228
Cited by: 0

Abstract

Machine learning pipeline for discovering translation regulatory features and cross-UTR interactions from TIF-seq2 transcript isoform data, developed for the manuscript "Discovery of Translation Regulatory Features and Cross-UTR Interactions through Precise Mapping of mRNA Ends and Machine Learning."

Fields of Study & Subjects

RNA Research and Splicing RNA and protein synthesis mechanisms Genomics and Phylogenetic Studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
16 February 2026
Language
EN