Ozdemir Cagri, Vashishath Yashu, Bozdag Serdar
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States.
BioDiscovery Institute, University of North Texas, Denton, TX 76203, United States.
Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf313.
Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yielded integrative prediction solutions for multi-omics data, these methods lack a comprehensive and cohesive understanding of the rationale behind their specific predictions. To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN).
To demonstrate the superiority of IGCN, we compare its performance with other state-of-the-art approaches across different cancer subtype and biomedical classification tasks. Our experimental results show that our proposed model outperforms the state-of-the-art and baseline methods. IGCN identifies which types of omics data receive more emphasis for each patient when predicting a specific class. Additionally, IGCN has the capability to pinpoint significant biomarkers from a range of omics data types.
The source code is available at https://github.com/bozdaglab/IGCN.
开发用于跨多种组学数据进行综合分析的计算工具在癌症分子生物学和精准医学研究中具有极其重要的意义。虽然最近的进展已经产生了用于多组学数据的综合预测解决方案，但这些方法对其特定预测背后的基本原理缺乏全面和连贯的理解。为了阐明个性化医学并揭示多组学数据综合分析中以前未知的特征，我们引入了一种用于癌症分子亚型和生物医学分类应用的新型综合神经网络方法，称为综合图卷积网络（IGCN）。
为了证明IGCN的优越性，我们将其性能与其他最先进的方法在不同癌症亚型和生物医学分类任务上进行了比较。我们的实验结果表明，我们提出的模型优于最先进的方法和基线方法。IGCN在预测特定类别时确定了每位患者哪些类型的组学数据受到更多重视。此外，IGCN有能力从一系列组学数据类型中找出重要的生物标志物。
