A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC)
Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identifica...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9257352/ |
| _version_ | 1818448733080125440 |
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| author | Muhammad Nabeel Asim Muhammad Imran Malik Christoph Zehe Johan Trygg Andreas Dengel Sheraz Ahmed |
| author_facet | Muhammad Nabeel Asim Muhammad Imran Malik Christoph Zehe Johan Trygg Andreas Dengel Sheraz Ahmed |
| author_sort | Muhammad Nabeel Asim |
| collection | DOAJ |
| description | Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification. |
| first_indexed | 2024-12-14T20:24:12Z |
| format | Article |
| id | doaj.art-d96d7523296240818f61853146e1e337 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T20:24:12Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d96d7523296240818f61853146e1e3372022-12-21T22:48:39ZengIEEEIEEE Access2169-35362021-01-019193791939010.1109/ACCESS.2020.30376429257352A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC)Muhammad Nabeel Asim0https://orcid.org/0000-0001-5507-198XMuhammad Imran Malik1https://orcid.org/0000-0002-8079-5119Christoph Zehe2https://orcid.org/0000-0002-4959-8475Johan Trygg3Andreas Dengel4https://orcid.org/0000-0002-6100-8255Sheraz Ahmed5https://orcid.org/0000-0002-4239-6520German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, GermanyNational Center for Artificial Intelligence (NCAI), National University of Sciences and Technology, Islamabad, PakistanSartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, GermanyComputational Life Science Cluster (CLiC), Umeå University, Umeå, SwedenGerman Research Center for Artificial Intelligence (DFKI), Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence (DFKI), Kaiserslautern, GermanySmall non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.https://ieeexplore.ieee.org/document/9257352/RNA sequence analysissmall non-coding RNA classificationDenseNetResNet |
| spellingShingle | Muhammad Nabeel Asim Muhammad Imran Malik Christoph Zehe Johan Trygg Andreas Dengel Sheraz Ahmed A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) IEEE Access RNA sequence analysis small non-coding RNA classification DenseNet ResNet |
| title | A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) |
| title_full | A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) |
| title_fullStr | A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) |
| title_full_unstemmed | A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) |
| title_short | A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC) |
| title_sort | robust and precise convnet for small non coding rna classification rpc snrc |
| topic | RNA sequence analysis small non-coding RNA classification DenseNet ResNet |
| url | https://ieeexplore.ieee.org/document/9257352/ |
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