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。

IF 3.6 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY RNA Biology Pub Date : 2024-01-01 Epub Date: 2024-03-25 DOI:10.1080/15476286.2024.2329451
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
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引用次数: 0

摘要

非编码 RNA(ncRNA)序列的准确分类对于高级非编码基因组注释和分析至关重要,这是基因组学的一个基本方面,有助于了解 ncRNA 在各种生物过程中的功能和调控机制。虽然传统的机器学习方法已被用于区分 ncRNA,但这些方法往往需要大量的特征工程。最近,深度学习算法在 ncRNA 分类方面取得了进展。本研究提出的 BioDeepFuse 是一种混合深度学习框架,它将卷积神经网络(CNN)或双向长短期记忆(BiLSTM)网络与手工特征相结合,以提高准确性。该框架采用 k-mer one-hot、k-mer 字典和特征提取技术相结合的方法来表示输入。提取的特征嵌入深度网络后,可优化利用 ncRNA 序列的空间和序列细微差别。我们使用基准数据集和来自细菌生物体的真实世界 RNA 样本评估了 BioDeepFuse 的性能。结果显示 ncRNA 分类的准确率很高,这突出表明我们的工具在应对复杂的 ncRNA 序列数据挑战方面具有很强的鲁棒性。CNN 或 BiLSTM 与外部特征的有效结合预示着未来研究的广阔前景,尤其是在完善 ncRNA 分类器和加深对细胞过程和疾病表现中 ncRNA 的了解方面。除了最初在细菌生物体中的应用外,我们框架中集成的方法和技术有可能使 BioDeepFuse 在各种更广泛的领域中发挥有效作用。
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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.

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来源期刊
RNA Biology
RNA Biology 生物-生化与分子生物学
CiteScore
8.60
自引率
0.00%
发文量
82
审稿时长
1 months
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
期刊最新文献
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