Protein subcellular localization prediction is essential for understanding protein function and cellular organization. However, existing methods exhibit two major limitations: (1) they overlook the critical role of evolutionarily conserved protein domains, which are fundamental functional and structural units that significantly influence functions and subcellular localization, and (2) they rarely learn residue order and backbone coordinates simultaneously, neglecting the complementary information inherent in multi-modal representations. In this paper, we propose a novel Domain-Aware Multi-View Contrastive Representation Learning for Protein Subcellular Localization prediction, named DMVCL. Firstly, it devises domain-sequence/structure attention modules, which identify functionally significant regions in protein structures/sequences that critically determine subcellular localization. Secondly, it introduces a multi-view contrastive learning framework that unites inter-view and intra-view objectives. Inter-view contrastive learning aligns protein sequences with their corresponding structures by maximizing mutual information, thereby capturing the consistency of protein residue order and backbone coordinates. Intra-view contrastive learning enhances the representation discriminability of each modality by explicitly separating proteins with no common location and attracting those with any shared localization. Extensive experiments demonstrate that DMVCL significantly outperforms existing baselines. Ablation studies and visualizations further highlight the contributions of domain-sequence/structure attention and multi-view contrastive learning in achieving superior predictive performance.

The widespread adoption of wearable devices necessitates accurate multi-class arrhythmia detection from photoplethysmography (PPG) signals for early cardiovascular intervention. While existing methods excel in single-arrhythmia classification, they face significant challenges in multi-class scenarios, particularly in distinguishing clinically confusable types. To address these challenges, we propose MSCTA-Net-a novel network architecture integrating multi-scale CNN, Transformer, and channel attention fusion mechanism. The multi-scale CNN branch employs parallel convolutions with four kernel sizes to extract local features across multiple frequency bands, while the Transformer module captures global temporal dependencies. A channel attention fusion mechanism dynamically integrates both feature types. Evaluated on a clinical dataset of 46,827 PPG segments covering six rhythm types (SR, PVC, PAC, VT, SVT, AF), MSCTA-Net achieves state-of-the-art performance with 92.88% accuracy, 87.79% mean F1-score, and mean precision, recall, specificity of 88.03%, 87.57%, 98.56%, respectively.

ECG-based biometric identification faces significant challenges in cross-state scenarios where physiological states differ between enrollment and authentication. Existing methods typically assume identical states, leading to substantial performance degradation when applied to real-world dual-state conditions. We propose CrossStateECG-Lite, an adaptive thresholding attention network specifically designed for robust cross-state ECG identification. The network integrates multi-scale feature learning, deep residual connections, and efficient attention mechanisms to capture state-invariant cardiac patterns. To address the inherent uncertainty in cross-state identification, we develop a personalized decision-making mechanism using Bayesian adaptive thresholding, which customizes authentication boundaries based on individual physiological variability. Evaluated on 45 subjects with paired rest-exercise recordings, CrossStateECG- Lite achieves balanced accuracies of 95.26 % (rest-to-post-exercise) and 96.05% (post-exercise-to-rest). The lightweight architecture with only 797K parameters demonstrates that efficient design combined with personalized decision-making provides an effective solution for real-world ECG biometrics where physiological states cannot be controlled. Comprehensive ablation studies confirm the synergistic contributions of the architectural components, with deep convolutional layers being particularly effective in learning state-invariant representations.

Protein function prediction is one of the most important biological problems in the field of bioinformatics. The functions of proteins are generally described by a series of Gene Ontology (GO) terms that have hierarchical relationships. Two factors hinder the effective prediction of protein functions using current methods: 1) they cannot well model and learn the topological semantic similarity between residues and GO terms, resulting in a huge semantic gap; 2) they predict the functions of proteins by calculating the semantic similarity between protein-level embeddings and GO terms, which does not effectively learn the protein-function relationship. To address the above issues, we propose the Topological-aware Residue-Gene Ontology Attention Network (TRGOA) for protein function prediction. First, a topological-aware attention module is designed to leverage attention scores within this joint semantic space allowing for modeling the fine-grained semantic similarity between residues and GO terms, thereby narrowing the semantic gap. Second, a multi-head aggregator is proposed, which adeptly captures the functions relevant fine-grained semantic similarity and filters out function-irrelevant components, which effectively reveal protein-function relationships, thereby enhancing generality and robustness. Finally, TRGOA has demonstrated promising outcomes, revealing our model can understand the protein-function relationship in deep insights.

Cervical cancer is one of the fastest growing and most dangerous cancers, seriously threatening women’s health and lives. Cervical cytopathology image classification is a very important approach for diagnosing cervical cancer. The advent of the automatic computer-aided diagnosis system can tackles this issue. However, cervical cell images of different classes exhibit similar appearances, posing a challenge for accurate classification. To address this challenge, this work proposes a framework named MSCCNet. In our MSCCNet, the cross-layer attention-based feature fusion module is used to obtain multi-scale discriminative features. Meanwhile, the spatial relationship modeling module is utilized to encode the relative relationship between objects and capture more slight differences between cervical cells, further strengthening the representation ability of features. We also introduce the joint loss to enhance the penalty for misclassified samples. The model training and evaluation are performed on our developed DSCC dataset and publicly available SIPaKMeD datasets. The proposed MSCCNet achieves overall accuracies of 87.88% and 97.90% on these two datasets, respectively, outperforming several existing classification methods.

Gene Ontology (GO) is a framework that utilizes a series of GO terms in a Directed Acyclic Graph (DAG) to describe protein functions. Proteins are typically annotated with several or dozens of GO terms. However, existing methods often struggle to simultaneously annotate multiple relevant GO terms with hierarchical dependencies to proteins, as they solely rely on protein sequences or structures. To better utilize the hierarchical information of GO terms and improve protein function annotation performance, we propose the Protein Structure-Label Embedding Attention Network for Protein Function Annotation (SLPFA). SLPFA embeds proteins and GO terms into a joint latent space using attention mechanisms to bridge the semantic gap between them. Specifically, we employ a soft-mask GNN to learn the topological structure of proteins, allowing simultaneous focus on key nodes while remaining invariant to irrelevant parts. Additionally, we encode the ancestral information for each GO term in its embedding and utilize a learnable matrix to capture the hierarchical dependencies. Finally, SLPFA employs protein structure-label embedding attention to project the protein structure and label embedding together into a joint latent space. This enables the model to learn the high-level semantics of proteins and hierarchical GO terms, resulting in a reduced semantic gap between proteins and their functions. Experimental results demonstrate that SLPFA outperforms state-of-the-art deep learning-based methods on the PDB-cdhit dataset, which yields Fmax of 0.604, 0.478, 0.524 and the AUPRC of 0.630, 0.357, 0.452 for the MF, BP, CC ontology domains, respectively. Furthermore, when the training and testing proteins have less than 15% sequence identity, SLPFA also achieves competitive results in the MF, BP, and CC ontology domains.

Lots of researchers have studied for classifying histopathological whole slide images (WSIs). Since a WSI is too large to be processed directly, researchers usually cut it into many small-sized patches and then integrate the discriminative features extracted from the patches to obtain a slide-level feature of the WSI. The integration strategy generating the slide-level features is crucial for the WSI classification model. Lots of attention-based methods have been proposed for such purpose. However, most attention-based methods do not take the patches relationship into consideration, which affects the classification performance of the models. In this work, we propose a novel Context-Guided attention (CGattention) method to integrate the patch-level features, which constructs a context vector to simulate the global context information of the whole WSI and implicitly characterizes the relationship between patches in the WSI. When evaluated on two publicly available datasets, the CGattention based model obtained the better performance than other attention-based models.

The cervical histopathology analysis result is the gold standard for cervical cancer diagnosis. Conventional histopathological examination depends on pathologists’ observation under microscope, which is notoriously labor-intensive and subjective. The popularization of digital pathology technology makes the collection of the cervical histopathological whole slide images (WSIs) more convenient, so it has become possible to develop computer-aided diagnosis methods for cervical cancer. In this work, we first collected the cervical histopathological WSIs from 917 patients with pathological diagnosis through a retrospective study, of which 286 WSIs contained annotations of several lesion areas that were manually outlined by the pathologists. Then we proposed a method for classifying cervical histopathological WSIs by combining deep multi-instance transfer learning (DMITL) and support vector machine (SVM). The DMITL aimed for learning the representations of the WSIs, and the SVM was used for building the classification model of the WSIs. We generated the training and test sets based on our collected WSIs to train and evaluate our method. The validation results have shown that the good performance of our proposed method.

Breast cancer has become one of the most common cancers in the world, and it is also the most lethal cancer in women. As a non-invasive imaging modality, ultrasonography can diagnose the degree of breast lesions and be used for large-scale screening. However, since the lesions in breast ultrasound(BUS) images are morphologically diverse, accompanied by relatively low contrast and complex textures, BUS image recognition faces greater challenges than natural images. In this study, We propose a novel network architecture that combines convolutional neural network(CNN) with vision transformer(ViT) to aggregate local feature details and long-range feature dependencies. Moreover, in order to perform multi-scale feature fusion, we introduce cross attention between the deep feature map and the shallow feature map in the network block to carry out the interaction between the deep feature and the shallow feature information. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 85.33%, under the comparable parameter complexity, which outperforms most convolutional neural networks(CNNs) and vision transformers (ViTs).

BACKGROUND: Via counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs. RESULTS: In the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model. CONCLUSION: The proposed method has great potential for application in intelligent and accurate classification of WBCs.

BACKGROUND: Making clear what kinds of metabolic pathways a drug compound involves in can help researchers understand how the drug is absorbed, distributed, metabolized, and excreted. The characteristics of a compound such as structure, composition and so on directly determine the metabolic pathways it participates in. METHODS: We developed a novel hybrid framework based on the graph attention network (GAT) to predict the metabolic pathway classes that a compound involves in, named HFGAT, by making use of its global and local characteristics. The framework mainly consists of a two-branch feature extracting layer and a fully connected (FC) layer. In the two-branch feature extracting layer, one branch is responsible to extract global features of the compound; and the other branch introduces a GAT consisting of two graph attention layers to extract local structural features of the compound. Both the global and the local features of the compound are then integrated into the FC layer which outputs the predicted result of metabolic pathway categories that the compound belongs to. RESULTS: We compared the multi-class classification performance of HFGAT with six other representative methods, including five classic machine learning methods and one graph convolutional network (GCN) based deep learning method, on the benchmark dataset containing 6999 compounds belonging to 11 pathway categories. The results showed that the deep learning-based methods (HFGAT, GCN-based method) outperformed the traditional machine learning methods in the prediction of metabolic pathways and our proposed HFGAT method performed better than the GCN-based method. Moreover, HFGAT achieved higher [Formula: see text] scores on 8 of 11 classes than the GCN-based method. CONCLUSIONS: Our proposed HFGAT makes use of both the global and local information of the compounds to predict their metabolic pathway categories and has achieved a significant performance. Compared with the GCN model, the introduction of the GAT can help our model pay more attention to substructures of the compound that are useful for the prediction task. The study provided a potential method for drug discovery with all types of metabolic reactions that may be involved in the decomposition and synthesis of pharmaceutical compounds in the organism.

White blood cells (WBCs) play a critical part in the human immune system, protecting human from bacteria and viruses. In general, WBCs can be divided into five categories: basophil, monocyte, eosinophil, neutrophil, and lymphocyte. The proportion of different kinds of WBCs is closely related to human health, thus analyzing the numbers and percentages of WBCs is usually included in the blood routine examination. Therefore, accurate identification of different types of WBCs has become an important step in the statistical analysis. However, the insufficiency and imbalance of WBC samples in medical computer vision domains is still a challenge to classify WBCs intelligently and accurately. This paper presents an attention based double-branch network, called TransMixNet, to build the classification model for WBCs recognition. During the model construction, we adopt transfer learning and data augmentation strategies to overcome the problems of insufficient and unbalanced samples. In addition, mixup strategy is used to model the domain relationship between different types of training samples to improve the generalization performance of the model. The evaluation experiment results show that our model is promising and applicable to clinical applications.

Exploring the relationship between molecular structure and metabolic pathways plays a significant role in researching the metabolism and pharmacokinetic effects of drugs. Therefore, one usually want to know what kinds of metabolic pathways that a new synthetic drug compound may involve in. In this paper, we propose a novel deep learning framework based on GAT (Graph Attention neTwork) for such purpose. The framework mainly consists of a two-branch feature extractor and a FC (Fully Connected) layer. In the two-branch feature extractor, one is used to generate three kinds global features of the compounds, and the other one is used to learn the local structural features via two GAT layers. All features are embedded into the FC layer to output multiple probabilities, each of which corresponds to the probability of one kind of pathway that the compound belongs to. The comparing results to other five state-of-the-art representative methods on the KEGG (Kyoto Encyclopedia of Genes and Genomes) data set have shown that our method can achieve the highest prediction accuracy, illustrating that it is a promising tool that can be helpful to analyze the metabolic pathways of the drug compounds.

Circular RNAs (circRNAs) are novel rising stars of noncoding RNAs, which are highly abundant and evolutionarily conserved across species. Number of publications related to circRNAs increased sharply in recent years, representing emerging focuses in the field. Therefore, tools, pipelines and databases have been developed to identify and store circRNAs. However, there is no existing tool to visualize and explore circRNAs. Therefore, we introduce CircView, a user-friendly visualization tool for circRNAs detected from existing tools. CircView enables users to visualize circRNAs and to quantify number of samples with detected circRNAs. CircView allows users to explore circRNAs detected by unique or multiple tools. Furthermore, CircView allows users to view the regulatory elements, such as microRNA response elements and RNA-binding protein binding sites. CircView is a unique tool to visualize and explore circRNAs, which helps users to better understand potential functions of circRNAs and design the functional experiments.

Long noncoding RNAs (lncRNAs) are a large family of noncoding RNAs that play a critical role in various normal bioprocesses as well as tumorigenesis. However, the expression patterns and biological functions of lncRNAs in acute leukemia have not been well studied. Here, we performed transcriptome-wide lncRNA expression profiling of acute myeloid leukemia (AML) patient samples, along with non-leukemia control hematopoietic samples. We found that lncRNAs were differentially expressed in AML samples relative to control samples. Notably, we identified that lncRNAs upregulated in AML (relative to the control samples) are associated with a lower degree of DNA methylation and a higher ratio of being bound by transcription factors such as SP1, STAT4, ATF-2 and ELK-1 compared with those downregulated in AML. Moreover, an enrichment of H3K4me3 and a depletion of H3K27me3 were observed in upregulated lncRNAs in AML. Expression patterns of three types of lncRNAs (antisense, enhancer and intergenic lncRNAs) have previously been characterized. Of the identified lncRNAs, we found that high expression level lncRNA LOC285758 is associated with the poor prognosis in AML patients. Furthermore, we found that LOC285758 regulates proliferation of AML cell lines by enhancing the expression of HDAC2, a key factor in carcinogenesis. Collectively, our study depicts a landscape of important lncRNAs in AML and provides novel potential therapeutic targets and prognostic markers for AML treatment.

Circular RNA (circRNA) is a group of RNA family generated by RNA circularization, which was discovered ubiquitously across different species and tissues. However, there is no global view of tissue specificity for circRNAs to date. Here we performed the comprehensive analysis to characterize the features of human and mouse tissue-specific (TS) circRNAs. We identified in total 302 853 TS circRNAs in the human and mouse genome, and showed that the brain has the highest abundance of TS circRNAs. We further confirmed the existence of circRNAs by reverse transcription polymerase chain reaction (RT-PCR). We also characterized the genomic location and conservation of these TS circRNAs and showed that the majority of TS circRNAs are generated from exonic regions. To further understand the potential functions of TS circRNAs, we identified microRNAs and RNA binding protein, which might bind to TS circRNAs. This process suggested their involvement in development and organ differentiation. Finally, we constructed an integrated database TSCD (Tissue-Specific CircRNA Database: http://gb.whu.edu.cn/TSCD) to deposit the features of TS circRNAs. This study is the first comprehensive view of TS circRNAs in human and mouse, which shed light on circRNA functions in organ development and disorders.