针对阿尔茨海默病的自导式知识注入图神经网络。

Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu, Lei Yang, Liang Zhan, Feng Zheng, Weiwen Jiang, Yanfu Zhang
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引用次数: 0

摘要

图神经网络(GNN)是处理不规则结构数据的熟练机器学习模型。然而,在应用于分析阿尔茨海默病(AD)的大脑连接组时,它们的通用表述并不完善,需要结合特定领域的知识才能实现最佳模型性能。将老年痴呆症相关专业知识整合到 GNN 中是一项重大挑战。目前依赖人工设计的方法往往需要外部领域专家提供大量专业知识,以指导新型模型的开发,从而耗费大量时间和资源。为了减少对人工策划的需求,本文介绍了一种新型的自引导知识注入多模态 GNN,可自主地将领域知识整合到模型开发过程中。我们建议将现有的领域知识概念化为自然语言,并设计一个专门的多模态 GNN 框架,利用这些未经整理的知识来指导 GNN 子模块的学习,从而增强其功效并提高预测的可解释性。为了评估我们的框架的有效性,我们汇编了一个全面的文献数据集,其中包括最近发表的有关注意力缺失症的同行评议出版物。通过将该文献数据集与几个真实世界的注意力缺失症数据集进行整合,我们的实验结果表明了所提出的方法在为特定领域应用提取策划知识和提供图解方面的有效性。此外,我们的方法还成功地利用了提取的信息来提高 GNN 的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

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