PTGCDA：结合 PU 学习、Transformer 与 GraphSAGE 的 circRNA-疾病关联预测框架
PTGCDA: a PU-learning-driven Transformer and GraphSAGE framework for circRNA-disease association prediction
PTGCDA 是一个整合序列表示学习、图表示学习、Transformer 与正样本-无标签（PU）学习的 circRNA-疾病关联预测计算框架。该框架用 k-mer 嵌入、Word2Vec 和 TextCNN 提取 circRNA 序列特征，由 GraphSAGE 聚合异质网络中 circRNA 与疾病的邻域信息学习节点表示，再用 Transformer 多头注意力捕捉二者表示间的相互作用；两步 PU 学习策略筛选可靠负样本以降低训练标签噪声。
来源：Bioinformatics Advances · doi.org
Motivation
Circular RNAs play key regulatory roles in many human diseases, making accurate prediction of circRNA-disease associations essential for understanding diseases and discovering biomarkers. Existing methods are limited by insufficient sequence utilization, network sparsity, and noisy negative samples from unlabeled data.
Results
We propose PTGCDA, a computational framework that integrates sequence representation learning, graph representation learning, Transformer, and positive-unlabeled learning for circRNA-disease association prediction. PTGCDA extracts circRNA sequence features using k-mer embeddings, Word2Vec, and TextCNN; GraphSAGE learns node representations of circRNAs and diseases by aggregating neighborhood information from the heterogeneous network; and multi-head attention in Transformer further captures interactions between circRNA and disease representations. Meanwhile, a two-step positive-unlabeled learning strategy identifies reliable negative samples to reduce label noise during training. Five-fold cross-validation on four benchmark datasets and one integrated dataset demonstrated that PTGCDA outperformed existing methods, achieving an AUROC of 0.9202 on Dataset4. De novo prediction on the same dataset achieved an AUROC of 0.8646 and an AUPR of 0.1874, while ablation and case studies further supported the effectiveness of PTGCDA and its ability to identify potential circRNA-disease associations.
Availability and implementation
The PTGCDA code is freely available at https://github.com/WANGSicong1113/PTGCDA.