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A robust and precis...
A robust and precise convnet for small non-coding rna classification (rpc-snrc)
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- Asim, Muhammad Nabeel (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany
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- Malik, Muhammad Imran (författare)
- National Center for Artificial Intelligence (NCAI), National University of Sciences and Technology, Islamabad, Pakistan
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- Zehe, Christoph (författare)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Trygg, Johan (författare)
- Umeå universitet,Kemiska institutionen,Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umea, Sweden
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- Dengel, Andreas (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany
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- Ahmed, Sheraz (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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(creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: IEEE Access. - 2169-3536. ; 9, s. 19379-19390
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Densenet
- Resnet
- RNA sequence analysis
- Small non-coding RNA classification
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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