The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and clinical symptoms), drive us to develop novel designs to address unique challenges in multiomics prediction. In this paper, we propose the multi-view knowledge transfer learning (MVKTrans) framework, which transfers intra- and inter-omics knowledge in an adaptive manner by reviewing data heterogeneity and suppressing bias transfer, thereby enhancing classification performance. Specifically, we design a graph contrastive module that is trained on unlabeled data to effectively learn and transfer the underlying intra-omics patterns to the supervised task. This unsupervised pretraining promotes learning general and unbiased representations for each modality, regardless of the downstream tasks. In light of the varying discriminative capacities of modalities across different diseases and/or samples, we introduce an adaptive and bi-directional cross-omics distillation module. This module automatically identifies richer modalities and facilitates dynamic knowledge transfer from more informative to less informative omics, thereby enabling a more robust and generalized integration. Extensive experiments on four real biomedical datasets demonstrate the superior performance and robustness of MVKTrans compared to the state-of-the-art. Code and data are available at https://github.com/Yaolab-fantastic/MVKTrans.

翻译：多组学数据的独特特征，包括生物层级内部及层级间的复杂相互作用以及疾病异质性（例如病因学和临床症状的异质性），促使我们开发新颖的设计以应对多组学预测中的独特挑战。本文提出多视角知识迁移学习（MVKTrans）框架，该框架通过审视数据异质性并抑制偏差迁移，以自适应方式迁移组学内与组学间知识，从而提升分类性能。具体而言，我们设计了一个基于无标签数据训练的图对比学习模块，以有效学习底层组学内模式并将其迁移至监督任务。这种无监督预训练促进了针对每种模态学习通用且无偏的表征，而不依赖于下游任务。鉴于不同疾病和/或样本中模态判别能力的差异性，我们引入了一个自适应双向跨组学蒸馏模块。该模块能自动识别信息更丰富的模态，并促进从信息量大的组学到信息量小的组学的动态知识迁移，从而实现更稳健、更泛化的整合。在四个真实生物医学数据集上的大量实验表明，MVKTrans相较于现有最优方法具有更优越的性能和稳健性。代码与数据可在 https://github.com/Yaolab-fantastic/MVKTrans 获取。