ArticleFrontiers in genetics2019
Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Invertebrate miRNA pva-small RNA-11881/pva-miR-11881 as a potential RNA-based therapeutic against white spot syndrome virus in infected shrimp.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deciphering the role of miRNA-134 in the pathophysiology of depression: A comprehensive review.Heliyon · 2024Review
- GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.Scientific reports · 2024Article
- BreCML: identifying breast cancer cell state in scRNA-seq via machine learning.Frontiers in medicine · 2024Article
- ScnML models single-cell transcriptome to predict spinal cord neuronal cell status.Frontiers in genetics · 2024Article
- NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning.Plant methods · 2023Article
- MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding.International journal of molecular sciences · 2023Article
- Article
- Deciphering the role of precursor miR-12136 and miR-8485 in the progression of intellectual disability (ID).IBRO neuroscience reports · 2022Article
- De novo assembly and characterization of the draft genome of the cashew (Anacardium occidentale L.).Scientific reports · 2022Article
- ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction.iScience · 2022Article
- Construction and characterization of a de novo draft genome of garden cress (Lepidium sativum L.).Functional & integrative genomics · 2022Article
- BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.Briefings in bioinformatics · 2022Article
- CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- MicroRNAs (miRNAs) in Cardiovascular Complications of Rheumatoid Arthritis (RA): What Is New?International journal of molecular sciences · 2022Review
- Article
- MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation.Frontiers in plant science · 2022Article
- Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework.Frontiers in plant science · 2022Article
- Exosomal microRNA-15a from ACHN cells aggravates clear cell renal cell carcinoma via the BTG2/PI3K/AKT axis.The Kaohsiung journal of medical sciences · 2021Article
- Whole-Genome Doubling Affects Pre-miRNA Expression in Plants.Plants (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.
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