Anderson P Avila Santos, Breno L S de Almeida, Robson P Bonidia, Peter F Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S Sanches, André C P L F de Carvalho
{"title":"BioDeepfuse:一种集成特征提取技术的混合深度学习方法,用于增强非编码 RNA 分类。","authors":"Anderson P Avila Santos, Breno L S de Almeida, Robson P Bonidia, Peter F Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S Sanches, André C P L F de Carvalho","doi":"10.1080/15476286.2024.2329451","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>k-</i>mer one-hot, <i>k-</i>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.</p>","PeriodicalId":21351,"journal":{"name":"RNA Biology","volume":"21 1","pages":"1-12"},"PeriodicalIF":3.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968306/pdf/","citationCount":"0","resultStr":"{\"title\":\"BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.\",\"authors\":\"Anderson P Avila Santos, Breno L S de Almeida, Robson P Bonidia, Peter F Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S Sanches, André C P L F de Carvalho\",\"doi\":\"10.1080/15476286.2024.2329451\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 <i>k-</i>mer one-hot, <i>k-</i>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. 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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.
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.
期刊介绍:
RNA has played a central role in all cellular processes since the beginning of life: decoding the genome, regulating gene expression, mediating molecular interactions, catalyzing chemical reactions. RNA Biology, as a leading journal in the field, provides a platform for presenting and discussing cutting-edge RNA research.
RNA Biology brings together a multidisciplinary community of scientists working in the areas of:
Transcription and splicing
Post-transcriptional regulation of gene expression
Non-coding RNAs
RNA localization
Translation and catalysis by RNA
Structural biology
Bioinformatics
RNA in disease and therapy