Liu Xianzhi, Liang Mingmin, Yu Ge, Tang Shichang, Wu Ouxiang, Zeng Bin, Wang Lei
School of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, China.
School of Intelligent Equipment, Hunan Vocational College of Electronic and Technology, Changsha, China.
Front Genet. 2025 Aug 8;16:1618472. doi: 10.3389/fgene.2025.1618472. eCollection 2025.
Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ecological health, and drug development.
In this manuscript, we introduce an innovative computational model named BANSMDA, which integrates Bilinear Attention Networks with sparse autoencoder to uncover hidden connections between microbes and diseases. In BANSMDA, we first constructed a heterogeneous microbe-disease network by integrating multiple Gaussian similarity measures for diseases and microbes, along with known microbe-disease associations. And then, we employed a BAN-based autoencoder and a sparse autoencoder module to learn node representations within this newly constructed heterogeneous network. Finally, we evaluated the prediction performance of BANSMDA using a 5-fold cross-validation framework.
Experiments results showed that BANSMDA achieved superior performance compared to other cutting-edge methods. To further assess its effectiveness, we carried out case studies on two common diseases (including Asthma and Colorectal carcinoma) and two important microbial genera (including and ), and in the top 20 predicted microbes, there were 19 and 20 having been confirmed by published literature respectively. Besides, in the top 20 predicted diseases, there were 19 and 19 having been confirmed by published literature separately. Therefore, it is easy to conclude that BANSMDA can achieve satisfactory prediction ability.
预测疾病与微生物之间的关系能够显著提升疾病的诊断与治疗水平，同时为公共卫生、生态健康及药物研发提供关键的科学支持。
在本论文中，我们引入了一种名为BANSMDA的创新计算模型，该模型将双线性注意力网络与稀疏自编码器相结合，以揭示微生物与疾病之间的隐藏联系。在BANSMDA中，我们首先通过整合多种针对疾病和微生物的高斯相似性度量以及已知的微生物-疾病关联构建了一个异构微生物-疾病网络。然后，我们采用基于BAN的自编码器和稀疏自编码器模块来学习这个新构建的异构网络中的节点表示。最后，我们使用5折交叉验证框架评估了BANSMDA的预测性能。
实验结果表明，与其他前沿方法相比，BANSMDA具有卓越的性能。为进一步评估其有效性，我们对两种常见疾病（包括哮喘和结肠直肠癌）和两个重要的微生物属（包括 和 ）进行了案例研究，并在预测的前20种微生物中，分别有19种和20种已被发表的文献证实。此外，在预测的前20种疾病中，分别有19种和19种已被发表的文献证实。因此，很容易得出结论，BANSMDA能够实现令人满意的预测能力。
