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Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach
Open accessAbstract
Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called "iMcRNA-PseSSC" and "iMcRNA-ExPseSSC", were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.
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“Most works in bioinformatics [41,46,47,48,49,50,51,52,53,54,55,56,57,58,59] also use SVM for classification.”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [46]
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Metadata
- Authors
- Liu, B., Fang, L., Liu, F., Wang, X., Chen, J., & Chou, K.-C.
- Affiliations
- Harbin Institute of Technology (CN)
- King Abdulaziz University (SA)
- Corresponding author
- Liu, Bin
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-30
- Volume
- 10
- Issue
- 3
- Pages
- e0121501
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4378912
- OpenAlex
- W2001168095
- Cited by
- 238
- References
- 93
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 19 · 2019: 29 · 2018: 46 · 2017: 26 · 2016: 48 · 2015: 46
- FWCI
- 24.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (HIT.NSRIF.2013103, 61272383, 61300112)
- Harbin Institute of Technology
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- pseudo amino acid composition, RNA secondary structure, human miRNAs, sequence-based prediction, benchmark dataset
- MeSH terms
- Amino Acids, Base Sequence, Humans, Nucleic Acid Conformation, Nucleotides, RNA Precursors, Genome, Human, Computational Biology, MicroRNAs, RNA, Small Untranslated
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