Liu Shaopeng, Hu Wanlu, Wang Chun-Chun, Zhuo Linlin, Lu Xu
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.
School of Science, Jiangnan University, Wuxi, 214122, China.
BMC Med Inform Decis Mak. 2025 Jul 18;25(1):270. doi: 10.1186/s12911-025-03093-6.
Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited data often restricts their effectiveness. This scarcity of information hinders the construction of a comprehensive association network, prompting the need for innovative solutions.
We propose RKGATMDA, a deep learning framework for microbe-disease association prediction. Utilizing a graph attention network, RKGATMDA learns representations from the microbe-disease association network. To address the limitation of insufficient association information, we introduce Random K-Nearest Neighbors to uncover latent relationships, enhancing representation learning. During each training iteration, associations are expanded based on attention scores, and a multi-head attention mechanism integrates diverse features, enabling RKGATMDA to capture comprehensive interactions between microbes and diseases.
Results Experimental results show that RKGATMDA achieves AUC values of 0.8906 in 5-fold cross-validation, 0.8999 in global leave-one-out cross-validation, and 0.7246 in local leave-one-out cross-validation, outperforming previous methods such as ABHMDA, KATZHMDA, LRLSHMDA, BiRWHMDA, and NTSHMDA. Case studies on asthma, colon cancer, and colorectal carcinoma further validate its predictive power.
Our findings demonstrate that RKGATMDA effectively predicts microbe-disease associations, with at least 9 out of the top 10 prediction pairs validated by biological evidence. This highlights the potential of RKGATMDA as a valuable tool in microbial-disease research, offering a promising approach for identifying novel associations and advancing our understanding of microbial pathogenesis.
预测微生物与疾病之间的关联对于临床诊断和治疗至关重要。然而，生物学实验耗时费力，因此需要高效的计算模型。传统模型依赖于现有的微生物-疾病关联，但数据有限常常限制了它们的有效性。这种信息稀缺阻碍了构建全面的关联网络，促使人们需要创新的解决方案。
我们提出了RKGATMDA，这是一种用于微生物-疾病关联预测的深度学习框架。利用图注意力网络，RKGATMDA从微生物-疾病关联网络中学习表示。为了解决关联信息不足的局限性，我们引入随机K近邻来揭示潜在关系，增强表示学习。在每次训练迭代中，基于注意力分数扩展关联，并且多头注意力机制整合不同特征，使RKGATMDA能够捕捉微生物与疾病之间的全面相互作用。
实验结果表明，RKGATMDA在5折交叉验证中的AUC值为0.8906，在全局留一法交叉验证中的AUC值为0.8999，在局部留一法交叉验证中的AUC值为0.7246，优于先前的方法，如ABHMDA、KATZHMDA、LRLSHMDA、BiRWHMDA和NTSHMDA。对哮喘、结肠癌和直肠癌的案例研究进一步验证了其预测能力。
我们的研究结果表明，RKGATMDA能够有效地预测微生物-疾病关联，前10个预测对中至少有9个得到了生物学证据的验证。这突出了RKGATMDA作为微生物-疾病研究中有价值工具的潜力，为识别新关联和推进我们对微生物发病机制的理解提供了一种有前景的方法。
