ArticleAvicenna journal of medical biotechnology
Computational Detection of piRNA in Human Using Support Vector Machine.
Article in Avicenna journal of medical biotechnology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniquesBiomedica : revista del Instituto Nacional de Salud · 2025Pooled it
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset.Medical & biological engineering & computing · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPiwi-interacting RNAs (piRNAs) are small non-coding RNAs (ncRNAs), with a length of about 24-32 nucleotides, which have been discovered recently. These ncRNAs play an important role in germline development, transposon silencing, epigenetic regulation, protecting the genome from invasive transposable elements, and the pathophysiology of diseases such as cancer. piRNA identification is challenging due to the lack of conserved piRNA sequences and structural elements.
methodsTo detect piRNAs, an appropriate feature set, including 8 diverse feature groups to encode each RNA was applied. In addition, a Support Vector Machine (SVM) classifier was used with optimized parameters for RNA classification. According to the obtained results, the classification performance using the optimized feature subsets was much higher than the one in previously published studies.
resultsOur results revealed 98% accuracy, Mathew' correlation coefficient of 98% and 99% specificity in discriminating piRNAs from the other RNAs. Also, the obtained results show that the proposed method outperforms its competitors.
conclusionIn this paper, a prediction method was proposed to identify piRNA in human. Also, 48 heterogeneous features (sequence and structural features) were used to encode RNAs. To assess the performance of the method, a benchmark dataset containing 515 piRNAs and 1206 types of other RNAs was constructed. Our method reached the accuracy of 99% on the benchmark dataset. Also, our analysis revealed that the structural features are the most contributing features in piRNA prediction.
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Identifiers
26855734PMC4717465What OpenQuestion holds
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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.