ArticleGenome biology2014
mirMark: a site-level and UTR-level classifier for miRNA target prediction.
Article in Genome biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 57 citations in OpenAlex.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- RIMap-RISC: a transcriptome-wide database of structurally modeled human microRNA interactions.Genome biology · 2026Article
- miR-490-5p inhibits the progression of osteosarcoma by targeting HDAC2.Translational cancer research · 2025Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.International journal of molecular sciences · 2024Article
- microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.Briefings in bioinformatics · 2024Article
- Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions.PLoS computational biology · 2024Article
- Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study.European radiology · 2024Article
- FL-circAS: an integrative resource and analysis for full-length sequences and alternative splicing of circular RNAs with nanopore sequencing.Nucleic acids research · 2024Article
- GraphTar: applying word2vec and graph neural networks to miRNA target prediction.BMC bioinformatics · 2023Article
- Review
- CircMiMi: a stand-alone software for constructing circular RNA-microRNA-mRNA interactions across species.BMC bioinformatics · 2022Article
- Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.Frontiers in molecular neuroscience · 2022Review
- Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species.BMC bioinformatics · 2021Article
- miTAR: a hybrid deep learning-based approach for predicting miRNA targets.BMC bioinformatics · 2021Article
- Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.BMC bioinformatics · 2020Article
- Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data.GigaScience · 2018Article
- Prediction of human miRNA target genes using computationally reconstructed ancestral mammalian sequences.Nucleic acids research · 2017Article
- THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Pan-Cancer Analyses Reveal Long Intergenic Non-Coding RNAs Relevant to Tumor Diagnosis, Subtyping and Prognosis.EBioMedicine · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.