BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification-Reference-Cited by-同舟云学术

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

Published:2024-03-25 Issue:1 Volume:21 Page:1-12
ISSN:1547-6286
Container-title:RNA Biology
language:en
Short-container-title:RNA Biology

Author:

Avila Santos Anderson P.¹²,de Almeida Breno L. S.¹,Bonidia Robson P.¹³,Stadler Peter F.⁴,Stefanic Polonca⁵,Mandic-Mulec Ines⁵,Rocha Ulisses²,Sanches Danilo S.³,de Carvalho André C.P.L.F.¹

Affiliation:

1. Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil

2. Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research – UFZ GmbH, Leipzig, Saxony, Germany

3. Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio, Brazil

4. Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Saxony, Germany

5. Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Universidade de São Paulo

International Development Research Centre

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15476286.2024.2329451

Reference73 articles.

1. Chen X Huang L. Computational model for ncrna research. Oxford University Press; 2022. (Vol. 23) (No. 6). doi: 10.1093/bib/bbac472.

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

3. Non-coding RNA delivery for bone tissue engineering: Progress, challenges, and potential solutions

4. Non-coding RNA regulatory networks

5. Strategies and challenges for non-viral delivery of non-coding RNAs to the heart

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