{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["19(8)"],"submitter":["Ataei S"],"pubmed_abstract":["<h4>Background</h4>MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest.<h4>Results</h4>Here, we present AmiR-P3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge "],"journal":["PloS one"],"pagination":["e0308016"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11293646"],"repository":["biostudies-literature"],"pubmed_title":["AmiR-P3: An AI-based microRNA prediction pipeline in plants."],"pmcid":["PMC11293646"],"pubmed_authors":["Ahmadi J","Marashi SA","Abolhasani I","Ataei S"],"additional_accession":[]},"is_claimable":false,"name":"AmiR-P3: An AI-based microRNA prediction pipeline in plants.","description":"<h4>Background</h4>MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest.<h4>Results</h4>Here, we present AmiR-P3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-05-02T12:42:08.269Z","creation":"2025-04-19T20:46:54.874Z"},"accession":"S-EPMC11293646","cross_references":{"pubmed":["39088479"],"doi":["10.1371/journal.pone.0308016"]}}