ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning

Chantsalnyam, Tuvshinbayar; Siraj, Arslan; Tayara, Hilal; Chong, Kil To

doi:10.1016/j.ygeno.2021.07.004

Cited by 13 publications

(26 citation statements)

References 43 publications

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“…In this section, we compare the performance of different available state-of-the-art ncRNA classifiers: nRC [28], RNAGCN [39], ncrna-deep [30], MFPred [38], ncRDense [32], and NCYPred [35]. For this purpose, three datasets are used: Dataset1, Dataset1-nd and Dataset2.…”

Section: Resultsmentioning

confidence: 99%

“…This representation is also used by RPC-snRC [28], and fed to three consecutive Dense blocks (based on DenseNets, multiple convolutional layers with dense connections), with a final FCL for classification. ncRDense [29] is an evolution of ncRDeep. In addition to sequence information, the method includes a second input: a matrix with multiple representations of nucleotides and one-hot encoding of the secondary structure.…”

Section: Ncrna Classification Methods State-of-the-artmentioning

confidence: 99%

“…Applications of DL to ncRNA classification started with multiple CNN-based methods: nRC [28], ncRDeep [29], ncrna-deep 1 [30], RPC-snRC [31], ncRDense [32] and imCnC [33]. nRC pioneered the use of DL in the field.…”

Section: Deep Learning For Ncrna Classificationmentioning

confidence: 99%

“…ncRDense [32] Web server: ✓-Source code: X -Training: X Web server: https://nsclbio.jbnu.ac.kr/tools/ncRDense/. Although not mentioned on the site, there is a limit of 1,000 ncRNA per prediction task.…”

Section: Rag+dl [41]mentioning

confidence: 99%

“…ncrna-deep[30] Web server: X -Source code: ✓-Training: ✓ GitHub repository: https://github.com/bioinformatics-sannio/ncrna-deep.ncRDense[32] Web server: ✓-Source code: X -Training: X Web server: https://nsclbio.jbnu.ac.kr/tools/ncRDense/. Although not mentioned on the site, there is a limit of 1,000 ncRNA per prediction task.…”

mentioning

confidence: 99%

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Comparison and benchmark of deep learning methods for non-coding RNA classification

Creux,

Zehraoui,

Radvanyi

et al. 2023

Preprint

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The grouping of non-coding RNAs into functional classes started in the 1950s with housekeeping RNAs. Since, multiple additional classes were described. The involvement of non-coding RNAs in biological processes and diseases has made their characterization crucial, creating a need for computational methods that can classify large sets of non-coding RNAs. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We propose a comparison of the different approaches and of non-coding RNA datasets proposed in the state-of-the-art. Then, we perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on two popular datasets. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.

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