High-precision segmentation of liver vessels is crucial for surgical planning and clinical diagnosis. Yet, it remains challenging due to intricate vascular structures and the low contrast inherent in CT images. We propose GASNet, an innovative liver vessel segmentation network that integrates progressive resolution-aware supervision with a learnable Gabor multi-filter module. GASNet generates hierarchical segmentation masks and applies adaptive supervision at intermediate layers to enhance multi-scale feature learning. Built upon a ConvNeXt backbone, the network integrates a Multi-Scale Feature Refiner and Dilated Convolutional modules to improve structural modeling capacity. We evaluate GASNet on two public datasets (LiVS and MSD) as well as aselfconstructed dataset, LVTSD. GASNet consistently outperforms state-of-the-art methods and demonstrates superior capability in distinguishing hepatic and portal veins on the revised MSD dataset, achieving a Dice score of 0.836 for hepatic veins and 0.820 for portal veins. The implementation is available at https://github.com/HappyBot516/GASNet.

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.

Improved cancer genomic diagnosis and prognosis are vital to accurate medical therapy. Deep learning methods offered an end-to-end solution to enhance the precision of analysis. With the fast pace of pre-trained Transformer models, it remains uncertain whether some novel approaches such as the sparsely gated mixture of expert (MOE) and self-attention mechanisms can further improve the precision of cancer prognosis and classification. In this paper, we introduce a novel sparsely gated cancer diagnosis and prognosis framework called Gene-MOE exploiting the potential of the MOE layers and the proposed mixture of attention expert (MOAE) layers to enhance the analysis accuracy. Additionally, we address overfitting challenges by integrating pan-cancer information from 33 distinct cancer types through pre-training. For survival analysis, Gene-MOE achieves the best Concordance Index compared with state-of-the-art models on 12 of 14 cancer types. For cancer classification, the total accuracy of the classification model for 33 cancer classifications reached 95.8%, representing the best performance compared to state-of-the-art models. For cancer subtyping, Gene-MOE achieves the best result on at least one metric of the log10 P-values and the number of significant clinical on seven of nine cancers. These results indicate that Gene-MOE holds strong potential for these downstream tasks.

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.

Liver cancer is one of the most common malignant diseases worldwide. Segmentation and reconstruction of liver tumors and vessels in CT images can provide convenience for physicians in preoperative planning and surgical intervention. In this paper, we introduced a TransFusionNet framework, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels. In addition, we applied the transfer learning approach to pre-train using public datasets and then fine-tune the model to further improve the fitting effect. Furthermore, we proposed an intelligent quantization scheme to compress the model weights and achieved high performance inference on JetsonTX2. The TransFusionNet framework achieved mean IoU of 0.854 in vessel segmentation task, and achieved mean IoU of 0.927 in liver tumor segmentation task. When profiling the Computational Performance of the quantized inference, our quantized model achieved 4TFLOPs on Node with NVIDIA RTX3090 and 132GFLOPs on JetsonTX2. This unprecedented segmentation effect solves the accuracy and performance bottleneck of automated segmentation to a certain extent.

Traditional Chinese medicines (TCMs) have been used to treat diseases for thousands of years. The application of traditional Chinese medicine provides new ideas for the treatment of cancer and other intractable diseases. Phosphoinositide-3kinase (PI3K) pathway is an important way to regulate tumor cells, such as cervical cancer. Deep learning provides a powerful application in calculating interactions between drugs and targets. In this study, we try to use the method of deep learning to reposition molecules of TCMs and 21 targets on PI3K pathway, and predict the TCMs that can regulate PI3K pathway, so as to achieve the purpose of cancer treatment. A deep convolutional neural network (DCNN) is constructed and trained on KIBA dataset. The accuracy of predicting the binding affinity of drug-target pairs is 85.3%. DCNN ranked 433 molecules of 35 TCMs with 21 PI3K pathway target proteins. We find that Gancao and Huangqin have strong binding affinity with more than half of PI3K pathway targets. Meanwhile, Renshen, Zhizi, Mahuang, etc. are also effective on multiple targets.