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ceRNAs in plants: computational approaches and associated challenges for target mimic research.

, , , and . Briefings Bioinform., 19 (6): 1273-1289 (2018)

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Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants., , , , , and . Briefings Bioinform., 20 (2): 682-689 (2019)Feature extraction approaches for biological sequences: a comparative study of mathematical features., , , , , , and . Briefings Bioinform., (2021)Comparison tools for lncRNA identification: analysis among plants and humans., , and . CIBCB, page 1-8. IEEE, (2020)Make No Mistake! Why Do Tools Make Incorrect Long Non-coding RNA Classification?, , , , and . BSB, volume 13954 of Lecture Notes in Computer Science, page 36-45. Springer, (2023)ceRNAs in plants: computational approaches and associated challenges for target mimic research., , , and . Briefings Bioinform., 19 (6): 1273-1289 (2018)Impact of sequencing technologies on long non-coding RNA computational identification., , , , and . BIBM, page 3299-3306. IEEE, (2022)PlaNC-TE: a comprehensive knowledgebase of non-coding RNAs and transposable elements in plants., , , and . Database J. Biol. Databases Curation, (2018)Selecting the Most Relevant Features for the Identification of Long Non-Coding RNAs in Plants., , , and . BRACIS, page 539-544. IEEE, (2019)Bioinformática aplicada em RNomics: estratégias computacionais para caracterização de RNAs não-codificadores.. University of São Paulo, Brazil, (2012)ndltd.org (oai:teses.usp.br:tde-24092013-211625).Classification of Transposable Elements by Convolutional Neural Networks., , , and . ICAISC (2), volume 11509 of Lecture Notes in Computer Science, page 157-168. Springer, (2019)