Wang Ning, Wu Minghui, Gu Wenchao, Dai Chenglong, Shao Zongru, Subbalakshmi K P
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214121, Jiangsu, China.
Silicon Austria Labs, Linz, Austria.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf217.
More and more recent studies highlight the crucial role of the human microbiome in maintaining health, while modern advancements in metagenomic sequencing technologies have been accumulating data that are associated with human diseases. Although metagenomic data offer rich, multifaceted information, including taxonomic and functional abundance profiles, their full potential remains underutilized, as most approaches rely only on one type of information to discover and understand their related correlations with respect to disease occurrences. To address this limitation, we propose a multistage fusion tabular transformer architecture (MSFT-Transformer), aiming to effectively integrate various types of high-dimensional tabular information extracted from metagenomic data. Its multistage fusion strategy consists of three modules: a fusion-aware feature extraction module in the early stage to improve the extracted information from inputs, an alignment-enhanced fusion module in the mid stage to enforce the retainment of desired information in cross-modal learning, and an integrated feature decision layer in the late stage to incorporate desired cross-modal information. We conduct extensive experiments to evaluate the performance of MSFT-Transformer over state-of-the-art models on five standard datasets. Our results indicate that MSFT-Transformer provides stable performance gains with reduced computational costs. An ablation study illustrates the contributions of all three models compared with a reference multistage fusion transformer without these novel strategies. The result analysis implies the significant potential of the proposed model in future disease prediction with metagenomic data.
越来越多的近期研究强调了人类微生物组在维持健康方面的关键作用，而宏基因组测序技术的现代进展一直在积累与人类疾病相关的数据。尽管宏基因组数据提供了丰富、多方面的信息，包括分类学和功能丰度概况，但它们的全部潜力仍未得到充分利用，因为大多数方法仅依赖于一种类型的信息来发现和理解它们与疾病发生的相关关系。为了解决这一局限性，我们提出了一种多阶段融合表格变换器架构（MSFT-Transformer），旨在有效整合从宏基因组数据中提取的各种类型的高维表格信息。其多阶段融合策略由三个模块组成：早期的融合感知特征提取模块，用于改进从输入中提取的信息；中期的对齐增强融合模块，用于在跨模态学习中强制保留所需信息；后期的集成特征决策层，用于纳入所需的跨模态信息。我们进行了广泛的实验，以评估MSFT-Transformer在五个标准数据集上相对于现有模型的性能。我们的结果表明，MSFT-Transformer在降低计算成本的同时提供了稳定的性能提升。一项消融研究说明了与没有这些新颖策略的参考多阶段融合变换器相比，所有三个模型的贡献。结果分析表明，所提出的模型在未来利用宏基因组数据进行疾病预测方面具有巨大潜力。
