Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Protein-protein interactions (PPIs) play a critical role in cellular functions, which are essential for maintaining the proper physiological state of organisms. Therefore, identifying PPI sites with high accuracy is crucial. Recently, graph neural networks (GNNs) have achieved significant progress in predicting PPI sites, but there is still potential for further enhancement. In this study, we introduce GTE-PPIS, an innovative PPI site predictor that utilizes two components: a graph transformer and an equivariant GNN, to collaboratively extract features. These extracted features are subsequently processed through a multilayer perceptron to generate the final predictions. Our experimental results show that GTE-PPIS consistently outperforms existing methods on multiple evaluation metrics across benchmark datasets, strongly supporting the effectiveness of our approach.

The annotation of cell types based on single-cell RNA sequencing (scRNA-seq) data is a critical downstream task in single-cell analysis, with significant implications for a deeper understanding of biological processes. Most analytical methods cluster cells by unsupervised clustering, which requires manual annotation for cell type determination. This procedure is time-overwhelming and non-repeatable. To accommodate the exponential growth of sequencing cells, reduce the impact of data bias, and integrate large-scale datasets for further improvement of type annotation accuracy, we proposed scSwinTNet. It is a pre-trained tool for annotating cell types in scRNA-seq data, which uses self-attention based on shifted windows and enables intelligent information extraction from gene data. We demonstrated the effectiveness and robustness of scSwinTNet by using 399 760 cells from human and mouse tissues. To the best of our knowledge, scSwinTNet is the first model to annotate cell types in scRNA-seq data using a pre-trained shifted window attention-based model. It does not require a priori knowledge and accurately annotates cell types without manual annotation.

The identification of protein-protein interaction sites (PPIS) plays a crucial role in understanding the mechanisms of biological processes. Traditional biological experimental methods for PPIS prediction are both expensive and time-consuming, developing computational methods can effectively reduce costs. However, existing approaches often focus on single-scale features and pay little attention to spatial neighborhood features, leading to unsatisfactory prediction performance. To address these challenges, we propose a dual-branch PPIS predictor (AEG-PPIS) based on augmented graph attention network (AGAT) and E(n) equivariant graph neural network (EGNN). AEG-PPIS extracts global features through EGNN, ensuring rotational and translational invariance of the protein graph. For local feature extraction, it employs an enhanced Augmented Graph Attention Network, which integrates initial node features, previous layer outputs, and features extracted by GraphSAGE using residual connections and identity mapping. Our model realizes the modeling of multi-scale information. Comparative experimental results show that the performance of AEG-PPIS is better than that of state-of-the-art methods. Ablation experiments and case studies demonstrate the effectiveness of AEG-PPIS in predicting PPIS.

Gene expression is a pivotal biological process within organisms, and in recent years, the prediction of gene expression levels has garnered increasing attention due to its vast potential in clinical applications. Predicting gene expression levels is a complex problem as gene expression is influenced by multiple factors, including but not limited to gene sequences, epigenetic modifications, transcription factor binding, and micro-environmental conditions. This paper proposes a model named Multimodal Expression, based on the Transformer architecture, which integrates various data types. The model can extract effective features from gene promoter sequences and combine pre-transcriptional and post-transcriptional regulatory information to predict gene expression levels. Experimental results demonstrate that our model can extract more effective information from promoter sequences, and the attention mechanism in the Transformer can integrate multiple data types to jointly predict gene expression levels. Compared to previous methods, our model’s R2values improved by 7.05%, 8.9%, and 1.91% when using gene sequence data alone, gene sequence data combined with mRNA half-life data, and gene sequence data combined with mRNA half-life data and transcription factor data, respectively.

Enhancer-promoter interactions are one of the essential mechanisms in the regulation of gene expression, and Accurate identification of enhancer-promoter interactions (EPIs) is challenging. In recent years, many deep learning methods have been used for EPI prediction. In this study, we propose DeepDualEPI, a dual-channel deep learning model based on genomic signals and DNA sequences, for predicting enhancer-promoter interactions (EPI). We used network architectures such as Dilated CNN, BiLSTM, and Transformer to process genomic signals, and network architectures such as multiscale CNN to extract DNA sequence features, and finally obtained hybrid features and output EPI prediction probabilities. To obtain the best combination of parameters for the model, we conducted several ablation experiments to optimize the model parameters. And to validate the performance of DeepDualEPI, we conducted experiments on four independent test sets to verify the generalization ability of the model. Compared with other state-of-the-art EPI prediction models, the DeepDualEPI model shows significant improvement in both AUC and AUPR evaluation metrics and experimentally demonstrates that better results are achieved on every chromosome, which proves that our model can stably perform EPI prediction across cell lines. And this paper demonstrates through ablation experiments that the inclusion of DNA sequence information can improve the performance of the model. Therefore, the two-channel hybrid feature deep learning approach via genomic signals and DNA sequences proposed in this paper helps to improve the overall accuracy of EPI prediction.