Dynamic Gene Attention Focus (DyGAF): Enhancing Biomarker Identification Through Dual-Model Attention Networks.

IF 2.3 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2025-03-27 eCollection Date: 2025-01-01 DOI:10.1177/11779322251325390
Md Khairul Islam, Himanshu Wagh, Hairong Wei
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引用次数: 0

Abstract

The DyGAF model, which stands for Dynamic Gene Attention Focus, is specifically designed and tailored to address the challenges in biomarker detection, progression reporting of pathogen infection, and disease diagnostics. The DyGAF model introduced a novel dual-model attention-based mechanism within neural networks, combined with machine learning algorithms to enhance the process of biomarker identification. The model transcended traditional diagnostic approaches by meticulously analyzing gene expression data. DyGAF not only identified but also ranked genes based on their significance, revealing a comprehensive list of the top genes essential for disease detection and prognosis. In addition, KEGG pathways, Wiki Pathways, and Gene Ontology-based analyses provided a multileveled evaluation of the genes' roles. In our analyses, we tailored COVID-19 gene expression profile from nasopharyngeal swabs that offer a more nuanced view of the intricate interplay between the host and the virus. The genes ranked by the DyGAF model were compared against those selected by differential expression analysis and random forest feature selection methods for further validation of our model. DyGAF demonstrated its prowess in identifying important biomarkers that could enrich gene ontologies and pathways crucial for elucidating the pathogenesis of COVID-19. Furthermore, DyGAF was also employed for diagnosing COVID-19 patients by classifying gene-expression profiles with an accuracy of 94.23%. Benchmarking against other conventional models revealed DyGAF's superior performance, highlighting its effectiveness in identifying and categorizing COVID-19 cases. In summary, DyGAF model represents a significant advancement in genomic research, providing a more comprehensive and precise tool for identifying key genetic markers and unraveling the complex biological insights of a disease. The DyGAF model is available as a software package at the following link: https://github.com/hiddenntreasure/DyGAF.

动态基因注意焦点(DyGAF):通过双模型注意网络增强生物标志物识别。
DyGAF模型,代表动态基因关注焦点,是专门设计和定制的,用于解决生物标志物检测,病原体感染进展报告和疾病诊断方面的挑战。DyGAF模型在神经网络中引入了一种新的基于注意力的双模型机制,并结合机器学习算法来增强生物标志物的识别过程。该模型通过细致地分析基因表达数据,超越了传统的诊断方法。DyGAF不仅对基因进行识别,还根据其重要性对基因进行排序,从而揭示了对疾病检测和预后至关重要的顶级基因的综合列表。此外,KEGG通路、Wiki通路和基于基因本体论的分析提供了对基因作用的多层次评估。在我们的分析中,我们从鼻咽拭子中定制了COVID-19基因表达谱,为宿主与病毒之间复杂的相互作用提供了更细致的视角。将DyGAF模型排序的基因与差异表达分析和随机森林特征选择方法选择的基因进行比较,以进一步验证我们的模型。DyGAF展示了其在识别重要生物标志物方面的能力,这些生物标志物可以丰富对阐明COVID-19发病机制至关重要的基因本体和途径。此外,DyGAF还用于诊断COVID-19患者,对基因表达谱进行分类,准确率为94.23%。与其他传统模型的对比显示,DyGAF的性能优越,突出了其在COVID-19病例识别和分类方面的有效性。总之,DyGAF模型代表了基因组研究的重大进步,为识别关键遗传标记和揭示疾病的复杂生物学见解提供了更全面和精确的工具。DyGAF模型作为软件包可在以下链接获得:https://github.com/hiddenntreasure/DyGAF。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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