Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1080/15476286.2024.2329451
Licence creative commons licence
Title (Primary) BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
Author Avila Santos, A.P.; de Almeida, B.L.S.; Bonidia, R.P.; Stadler, P.F.; Stefanic, P.; Mandic-Mulec, I.; Nunes da Rocha, U.; Sanches, D.S.; de Carvalho, A.C.P.L.F.
Source Titel RNA Biology
Year 2024
Department AME
Volume 21
Issue 1
Page From 1
Page To 12
Language englisch
Topic T7 Bioeconomy
Keywords Non-coding RNA; deep learning; neural networks; RNA identification; feature extraction; model performance; gene regulation; biological processes
Abstract The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.
Persistent UFZ Identifier
Avila Santos, A.P., de Almeida, B.L.S., Bonidia, R.P., Stadler, P.F., Stefanic, P., Mandic-Mulec, I., Nunes da Rocha, U., Sanches, D.S., de Carvalho, A.C.P.L.F. (2024):
BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
RNA Biol. 21 (1), 1 - 12 10.1080/15476286.2024.2329451