IEEE/ACM Trans Comput Biol Bioinform. 2024 Jan-Feb;21(1):57-68. doi: 10.1109/TCBB.2023.3335369. Epub 2024 Feb 5.
Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.
图学习方法因其能够表示非结构化信息（如主体间关系），在疾病诊断方面取得了显著的性能。尽管已经证明成像、遗传和临床数据对于退行性疾病的诊断至关重要，但现有方法很少考虑如何最好地利用它们之间的关系。如何最好地利用成像、遗传和临床数据的信息仍然是一个具有挑战性的问题。本研究提出了一种新的基于图的融合（GBF）方法来应对这一挑战。为了提取有效的成像-遗传特征，我们提出了一种成像-遗传融合模块，该模块使用注意力机制在成像和遗传数据的内部和之间获取模态特定和联合表示。然后，考虑到临床信息对诊断退行性疾病的有效性，我们提出了一种多图融合模块，进一步融合成像-遗传和临床特征，该模块采用可学习的图构建策略和图集成方法。在两个退行性疾病诊断基准（阿尔茨海默病神经影像学倡议和帕金森病进展标志物倡议）上的实验结果表明，与最先进的基于图的方法相比，它具有有效性。我们的研究结果应该有助于指导用于处理成像、遗传和临床数据的基于图的模型的进一步发展。
