We propose unsupervised multi-scenario (UMS) person re-identification (ReID) as a new task that expands ReID across diverse scenarios (cross-resolution, clothing change, etc.) within a single coherent framework. To tackle UMS-ReID, we introduce image-text knowledge modeling (ITKM) -- a three-stage framework that effectively exploits the representational power of vision-language models. We start with a pre-trained CLIP model with an image encoder and a text encoder. In Stage I, we introduce a scenario embedding in the image encoder and fine-tune the encoder to adaptively leverage knowledge from multiple scenarios. In Stage II, we optimize a set of learned text embeddings to associate with pseudo-labels from Stage I and introduce a multi-scenario separation loss to increase the divergence between inter-scenario text representations. In Stage III, we first introduce cluster-level and instance-level heterogeneous matching modules to obtain reliable heterogeneous positive pairs (e.g., a visible image and an infrared image of the same person) within each scenario. Next, we propose a dynamic text representation update strategy to maintain consistency between text and image supervision signals. Experimental results across multiple scenarios demonstrate the superiority and generalizability of ITKM; it not only outperforms existing scenario-specific methods but also enhances overall performance by integrating knowledge from multiple scenarios.

Existing circRNA-miRNA interaction prediction methods have not fully exploited the spatial folding information in circRNAs and pre-miRNAs. Furthermore, current methods inadequately address intramolecular modal consistency and intermolecular discrimination across distinct modalities, leading to suboptimal discriminative performance. Traditional attention mechanisms are computationally intensive and lack fine-grained, balanced modal weight allocation, impeding efficient feature fusion. To address these limitations, we propose GSSCMI, a novel method comprising three key components. First, an information integration module incorporates similarity information, sequence features, and secondary structure features. Second, a multimodal contrastive learning module processes features across three modalities, enhancing both intra-modal consistency for individual circRNA/miRNA and inter-modal discrimination between different circRNAs/miRNAs. Third, we innovatively design an efficient co-attention mechanism that simultaneously modulates fused modalities through unified and fine-grained attention scores, achieving balanced fusion while significantly reducing computational overhead. Experimental results demonstrate GSSCMI outperforms existing methods, with improvements of 9.53% in MCC and 6.40% in F1. Ablation studies further show the efficient co-attention reduces convergence iterations by approximately 58%. Compared to the initial co-attention, it reduces computational cost by 42.94% while improving MCC by 13.32% and ACC by 6.69%. Additionally, we identified regulatory sites through structure-based A-to-I editing and elucidated sequence-level inter-token dependencies via attention visualization.

Motivation The expression of circular RNAs (circRNAs) has been shown to be strongly correlated with drug sensitivity in human cells. However, experimental validation using wet-lab techniques is costly and inefficient, leaving a substantial portion of circRNA-drug sensitivity associations undiscovered. Therefore, improving the prediction efficiency of circRNA and sensitivity associations remains critical.Methods Here, we describe a method that integrates collaborative feature learning and graph structure learning to predict associations between circRNAs and drug sensitivity (CFGSCDSA). Specifically, collaborative learning integrated heterogeneous features from diverse data sources, thereby addressing the issue of data sparsity. Furthermore, graph structure learning with a confidence-guided pseudo-labeling strategy was employed to mitigate the detrimental effect of excessive negative samples. Results: Experimental evaluation revealed that CFGSCDSA attained superior performance compared to all competing models. Moreover, case studies provided further evidence of its capability to accurately predict both novel associations and new drug-related links.

Long noncoding RNAs (lncRNAs) play critical roles in regulating stemness signature genes (SSGs) and tumor immunity, thereby shaping the tumor microenvironment and antitumor immune responses. Increasing evidence suggests that cancer stem cell traits are closely associated with immune evasion and therapeutic resistance, underscoring the need to systematically characterize the pan-cancer interplay among SSGs, lncRNAs, and tumor immunity. Here, we developed an integrative analytical framework that combines network-based modeling with Bayesian network inference to identify core regulatory triplets (STEM-LncCRTs), each consisting of an lncRNA, an SSG, and an immune gene. We demonstrate that specific stemness-related lncRNAs can distinguish cancer subtypes, and that common stemness-related lncRNAs correlate significantly with immune cell infiltration. Notably, the ATAD5/PRR11-AS1/SKP2 triplet exhibits favorable prognostic potential across multiple cancers and consistently outperforms individual gene markers in predicting 1-, 3-, and 5-year overall survival. Furthermore, using four machine learning algorithms across three independent immunotherapy cohorts, we validate the predictive value of STEM-LncCRTs for immune checkpoint inhibitor response. Importantly, integrating STEM-LncCRTs with tumor mutation burden further improves predictive accuracy. Collectively, this study provides a systems-level view of stemness-related lncRNA regulation in tumor immunity and offers practical biomarkers for predicting immunotherapy efficacy.

MOTIVATION:Circular RNAs (circRNAs) interact with microRNAs (miRNAs) to regulate gene expression and influence disease progression. However, traditional models tend to overlook the significant contributions of certain features when dealing with diverse sequence information, resulting in the inability to capture some deep topological structures and thus leaving room for improvement in prediction performance. RESULTS:We propose HMA-GCA, a novel framework that integrates hybrid manifold augmentation and gated cross-attention for CMI prediction. The model first constructs multi-scale descriptors by combining sequence-derived features (K-mer, CTD, Doc2Vec) and topological features (Role2Vec, node degree, neighborhood proximity). It then applies PCA for global linear projection and UMAP for local nonlinear manifold learning, enhancing feature representations while preserving intrinsic data geometry. A channel-wise gated cross-attention mechanism dynamically controls the injection of miRNA information into circRNA representations. Extensive experiments on three benchmark datasets show that HMA-GCA consistently outperforms state-of-the-art methods across multiple metrics. To ensure interpretability, we conducted SHAP analysis to quantify the contribution of each feature type, revealing that sequence-derived features and topological similarities are the most influential. Ablation studies confirm the necessity of each module, while case studies demonstrate that top-ranked predictions are supported by literature evidence. Overall, HMA-GCA not only achieves state-of-the-art predictive performance but also provides interpretable insights into the molecular features. AVAILABILITY AND IMPLEMENTATION:The source code and data are freely available at https://github.com/Lixunwind/Prediction-circ-mi-by-Gate.git. The implementation is based on Python and the required dependencies are listed in the repository.

Circular RNAs (circRNAs) are an emerging class of non-coding RNAs with covalently closed loop structures and have been increasingly recognized for their regulatory roles in disease progression and drug response. Accurately identifying circRNA–drug sensitivity associations is therefore essential for understanding therapeutic mechanisms and advancing precision medicine. However, most existing computational methods fail to effectively integrate semantic and structural information and overlook cross-modal feature co-optimization, thereby limiting their predictive performance. To address these limitations, we develop an end-to-end graph representation learning framework for circRNA–drug sensitivity prediction by jointly modeling homogeneous similarity structures and heterogeneous interaction relationships. The framework integrates fused similarity graphs, semantic feature encoding with pre-norm residual attention, and structural representation learning via graph convolutional networks with Top-K sparse adjacency. In addition, a large-scale heterogeneous graph and a cross-modal collaborative feature mining module are employed to jointly optimize multi-source representations. Experimental results from 5-fold and 10-fold cross-validation, independent test evaluations, ablation study, and case study demonstrate that the proposed framework consistently achieves superior performance compared with state-of-the-art methods. The proposed framework provides a robust and effective computational strategy for circRNA–drug sensitivity prediction and offers a valuable tool for uncovering potential therapeutic associations, thereby facilitating future research in drug response analysis and precision medicine.

This study develops a remaining useful life (RUL) estimation algorithm for electric vehicle lithium-ion batteries based on multi-scale CNN feature integration and multiple multi-head attention LSTM (MCNN-MHALSTM). The proposed method considers the uncertainty of estimation results and realizes multi-feature fusion and temporal correlation learning via the designed network architecture. Firstly, using the strategy of skip connections, an independent multi-scale CNN is utilized to extract temporal dynamic features from three key battery states: capacity, internal resistance, and temperature Then, Multi-feature integration techniques are utilized to concatenate them and input them into the multiple multi-head attention LSTM (MHALSTM) network to accomplish feature correlation extraction. Secondly, the Long Short-Term Memory (LSTM) algorithm is introduced to estimate the RUL of electric vehicle batteries, and two multi-head attention layers with the same configuration are applied to improve the LSTM algorithm. While achieving high-order feature extraction of battery data, the RUL estimation task of electric vehicle batteries is divided into multiple small tasks, and the problem of feature redundancy mitigation is addressed through resource allocation. Finally, the effectiveness of the MCNN-MHALSTM electric vehicle battery RUL estimation model is verified using the NASA lithium-ion battery capacity degradation dataset.

BACKGROUND:Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experiments, analyzing circRNA-miRNA associations is often inefficient and labor-intensive. Although some computational models have been developed to identify these associations, they fail to capture the deep collaborative features between circRNA and miRNA interactions and do not guide the training of feature extraction networks based on these high-order relationships, leading to poor prediction performance. RESULTS:To address these issues, we innovatively propose a novel deep graph collaboration learning method for circRNA-miRNA interaction, called DGCLCMI. First, it uses word2vec to encode sequences into word embeddings. Next, we present a joint model that combines an improved neural graph collaborative filtering method with a feature extraction network for optimization. Deep interaction information is embedded as informative features within the sequence representations for prediction. Comprehensive experiments on three well-established datasets across seven metrics demonstrate that our algorithm significantly outperforms previous models, achieving an average AUC of 0.960. In addition, a case study reveals that 18 out of 20 predicted unknown CMI data points are accurate. CONCLUSIONS:The DGCLCMI improves circRNA and miRNA feature representation by capturing deep collaborative information, achieving superior performance compared to prior methods. It facilitates the discovery of unknown associations and sheds light on their roles in physiological processes.

Understanding long noncoding RNA (lncRNA) function is essential for revealing molecular mechanisms and developing effective therapies for complex diseases, as lncRNAs play important regulatory roles in many disease-related biological processes. However, existing lncRNA function predictors struggle to extract discriminative features from multimodal omics data and to model the semantic and topological structure of the gene ontology (GO), which severely limits their ability to achieve biologically meaningful and functionally informative predictions. To address these challenges, we propose a novel framework for lncRNA function prediction, namely MiCLSAO. Firstly, MiCLSAO utilizes multiview cross-contrastive learning with attention mechanisms to extract highly discriminative lncRNA features from diverse omics similarity networks. Secondly, graph convolutional networks are applied to learn initial features of GO terms, while multiscale topological and semantic relationships are incorporated to adaptively refine term representations. Finally, an lncRNA function predictor is developed by dynamically integrating the representations of lncRNAs and GO terms using a Kolmogorov-Arnold network. Extensive experiments demonstrate that MiCLSAO consistently outperforms state-of-the-art methods across multiple metrics, with significant capability to recover known functions and uncover novel ones. Moreover, MiCLSAO demonstrates remarkable practical utility and potential value by providing more functionally informative annotations for lncRNAs.

Clothing change person re-identification (CC-ReID) is a crucial task in intelligent surveillance, aiming to match images of the same person wearing different clothing. Promising performance in existing CC-ReID methods is achieved at the cost of labor-intensive manual annotation of identity labels. While some researchers have explored unsupervised CC-ReID, these methods still depend on additional deep learning models for preprocessing. To eliminate the need for additional models and improve performance, we propose a joint augmentation and part learning (JAPL) framework that obtains clothing change positive pairs in an unsupervised fashion by synergistically combining augmentation-based invariant learning (AugIL) and part-based invariant learning (ParIL). AugIL first constructs clothing change pseudo-positive pairs and then encourages the model to focus on clothing-invariant information by enhancing feature consistency between the pseudo-positive pairs. ParIL beneficially encourages high similarity between inter-cluster clothing change positive pair using part images and a prediction sharpening loss. PartIL also introduces a soft consistency loss that promotes clothing-invariant feature learning by encouraging consistency of class vectors between the real features actually used for CC-ReID and the part features. Experimental results on multiple ReID datasets demonstrate that the proposed JAPL not only surpasses existing unsupervised methods but also achieves competitive performance compared to some supervised CC-ReID methods.

Circular RNA (circRNA) is a class of noncoding RNA that is highly conserved and exhibit exceptional stability. Due to its function as a microRNA sponge, circRNA has gained significant attention as an essential biomarker and potential drug target in the pathogenesis of several cancers. Although many circRNAs have been identified to play a role in cancer resistance, traditional methods are time-consuming and expensive. In this context, computational methods offer a promising way to facilitate the discovery process. However, most existing prediction models focus on the association between circRNAs and drug resistance, without considering the corresponding disease-related information in the circRNA-drug resistance association. Incorporating disease-related information into the prediction of circRNA-drug resistance associations could potentially improve the efficiency and speed of discovering and developing circRNA-targeting drugs. We propose a computational framework, named GraphCDD, for predicting the association between circRNA and drug resistance. Our model utilizes data from three sources, namely circRNA, disease, and drug, to construct three similarity networks that represent the features of circRNA, disease, and drug, respectively. We utilize a multimodal graph neural network to acquire efficient representations of circRNAs, diseases, and drugs by integrating various types of information, and establish a predictive model. The experimental results have validated the effectiveness of our model and provided a promising method in predicting potential associations between circRNA and drug resistance. The source code and dataset of GraphCDD can be found at https://github.com/Ziqiang-Liu/GraphCDD .

Clothing change person re-identification (CC-ReID) aims to match different images of the same person, even when the clothing varies across images. To reduce manual labeling costs, existing unsupervised CC-ReID methods employ clustering algorithms to generate pseudo-labels. However, they often fail to assign the same pseudo-label to two images with the same identity but different clothing—referred to as a clothing change positive pair—thus hindering clothing-invariant feature learning. To address this issue, we propose the identity-clothing similarity modeling (ICSM) framework. To effectively connect clothing change positive pairs, ICSM first performs clothing-aware learning to leverage all discriminative information, including clothing, to obtain compact clusters. It then extracts cluster-level identity and clothing features and performs inter-cluster similarity estimation to identify clothing change positive clusters, reliable negative clusters, and hard negative clusters for each compact cluster. During optimization, we design an adaptive version of existing optimization methods to enhance similarities of clothing change positive pairs, while also introducing text semantics as a supervisory signal to further promote clothing invariance. Extensive experimental results across multiple datasets validate the effectiveness of the proposed framework, demonstrating its superiority over existing unsupervised methods and its competitiveness with some supervised approaches.

MicroRNAs (miRNAs) play a pivotal role as post-transcriptional regulators in tumorigenesis, influencing immune pathways in various cancers. However, a systematic identification of potential miRNAs influencing immune pathway activity remains largely elusive. In this study, we presented a comprehensive analysis of miRNAs within 17 immune-related pathways across 32 different cancers. Leveraging GSEA-based and target gene-based computational methods, we identify potential miRNA regulons that are intricately associated with tumor immunity. These miRNAs exhibit a propensity to regulate immune pathways across multiple cancer types. Moreover, miRNA immune regulons manifest expression perturbations in cancer and display significant correlations with immune cell infiltrations. Our study reveals the role of immune-related miRNAs in immune cell development, differentiation, and tumor growth and metastasis dynamics. Furthermore, we optimized two key immunology miRNA regulons, exemplified by hsa-miR-130b-3p and hsa-miR-106b-5p, demonstrating their wide-ranging influence on immune function in tumors. These miRNAs emerged as potential targets for a variety of drugs, offering promise as adjuvant therapy alongside conventional radiotherapy and chemotherapy, as well as contributing to immunotherapeutic approaches. Additionally, our study identifies two molecular subtypes within reproductive system cancers, characterized by distinct tumor mutational burdens (TMB L and TMB H phenotypes). These subtypes exhibit disparities in immune cell infiltrations, checkpoint expression, and prognosis, shedding light on potential avenues for personalized treatment strategies. In summary, our research provides an extensive panorama of miRNA immunology regulons, enhancing our understanding of miRNA function. This knowledge holds significant implications for the development of targeted therapies in tumor immunology and the refinement of personalized treatment approaches.

Protein-protein interactions (PPIs) play key roles in numerous biological processes and their dysregulation can lead to various human diseases. Modulating these interactions with small molecule PPI modulators has emerged as a promising strategy for treating such diseases. However, current computational approaches for screening PPI modulators often fail to integrate biomolecular expertise and lack the elucidation of interaction mechanisms. Here, we propose a knowledge-fused modulator-PPI interaction prediction method (KFPPIMI) to alleviate these issues. KFPPIMI constructs separate representation models for modulators and proteins, each of which integrates external knowledge from textual and graph-based data sources via a language modeling framework. The fusion of the nuanced expression of natural language with the structural attributes of biomolecules provides KFPPIMI with a holistic view of modulator-PPI interactions. Extensive experiments are conducted to evaluate the effectiveness of KFPPIMI and its individual components. The results show that KFPPIMI outperforms existing methods in different scenarios. Moreover, the modulator and protein representation model can be successfully applied to their respective downstream tasks with comparable performance.

MOTIVATION:Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance. RESULTS:This study presents a method that combines multi-view shared units and multichannel attention mechanisms to predict circRNA-disease associations (MSMCDA). MSMCDA first constructs similarity and meta-path networks for circRNAs and diseases by introducing shared units to facilitate interactive learning across distinct network features. Subsequently, multichannel attention mechanisms were used to optimize the weights within similarity networks. Finally, contrastive learning strengthened the similarity features. Experiments on five public datasets demonstrated that MSMCDA significantly outperformed other baseline methods. Additionally, case studies on colorectal cancer, gastric cancer, and nonsmall cell lung cancer confirmed the effectiveness of MSMCDA in uncovering new associations. AVAILABILITY AND IMPLEMENTATION:The source code and data are available at https://github.com/zhangxue2115/MSMCDA.git.

Color variations, a key challenge in the unsupervised visible-infrared person re-identification (UVI-ReID) task, have garnered significant attention. While existing UVIReID methods have made substantial efforts during the optimization phase to enhance the model's robustness to color variations, they often overlook the impact of color variations on the acquisition of pseudo-labels. To address this, in this paper, we focus on improving the robustness of pseudo-labels to color variations through data augmentation and propose an augmented and softened matching (ASM) method. Specifically, we first develop the crossmodality augmented matching (CAM) module, which performs channel augmentation on visible images to generate augmented images. Then, based on the fusion of the visibleinfrared and augmented-infrared centroid similarity matrices, CAM establishes cross-modality correspondences that are robust to color variations. To increase training stability, we design a soft-labels momentum update (SMU) strategy, which converts traditional one-hot labels into soft-labels through momentum updates, thus adapting to CAM. During the optimization phase, we introduce the cross-modality soft contrastive loss and cross-modality hard contrastive loss to promote modality-invariant learning from the perspectives of shared and diversified features, respectively. Extensive experimental results validate the effectiveness of the proposed method, showing that ASM not only outperforms state-of-the-art unsupervised methods but also competes with some supervised methods.

Visible-infrared person re-identification (VI-ReID) aims to match visible and infrared images of the same individual. Supervised VI-ReID (SVI-ReID) methods have achieved promising performance under the guidance of manually annotated identity labels. However, the substantial annotation cost severely limits their scalability in real-world applications. As a result, unsupervised VI-ReID (UVI-ReID) methods have attracted increasing attention. These methods typically rely on pseudo-labels generated by clustering and matching algorithms to replace manual annotations. Nevertheless, the quality of pseudo-labels is often difficult to guarantee, and low-quality pseudo-labels can significantly hinder model performance improvements. To address these challenges, we explore the use of attribute arrays extracted by a large vision-language model (LVLM) to enhance VI-ReID, and propose a novel LVLM-driven attribute-aware modeling (LVLM-AAM) approach. Specifically, we first design an attribute-aware reliable labeling strategy, which refines intra-modality clustering results based on image-level attributes and improves inter-modality matching by grouping clusters according to cluster-level attributes. Next, we develop an explicit-implicit attribute fusion module, which integrates explicit and implicit attributes to obtain more fine-grained identity-related text features. Finally, we introduce an attribute-aware contrastive learning module, which jointly leverages static and dynamic text features to promote modality-invariant feature learning. Extensive experiments conducted on VI-ReID datasets validate the effectiveness of the proposed LVLM-AAM and its individual components. LVLM-AAM not only significantly outperforms existing unsupervised methods but also surpasses several supervised methods.

Circular RNA, a class of RNA molecules gaining widespread attentions, has been widely recognized as a potential biomarker for many diseases. In recent years, significant progress has been made in the study of the associations between circRNA and diseases. However, traditional experimental methods are often inefficient and costly, making computational models an effective alternative. Nevertheless, existing computational methods still face challenges such as data sparsity and the difficulty of confirming negative samples, which limits the accuracy of predictions. To address these challenges, a novel computational method, namely MVHGCN, is proposed based on multi-view and graph convolutional networks to predict potential associations between circRNA and diseases. MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. Then it extracts different connection views of circRNA and diseases through meta-paths, maximizing the utilization of known association information, and aggregates deep feature information through graph convolutional networks. Finally, a MLP is used to predict the association scores. The experimental results show that MVHGCN significantly outperforms existing methods on benchmark datasets by 5-fold cross-validation. This research provides an effective new approach to studying the associations between circRNAs and diseases, capable of alleviating the problem of data sparsity and accurately identifying potential associations.

N6-methyladenosine (m6A) is a key epitranscriptomic marker enriched in long noncoding RNAs (lncRNAs) that is closely involved in complex disease mechanisms. Although accurate detection of m6A sites in lncRNAs is essential for understanding disease mechanisms, the development of effective computational predictors remains challenging due to the limited number of annotated sites. Moreover, most existing predictors are specifically designed for messenger RNAs (mRNAs) based on abundant mRNA-specific knowledge, yet they exhibit limited generalizability to lncRNAs. Given the similarities between mRNAs and lncRNAs, a transferable framework capable of leveraging their shared features is critical for advancing m6A site prediction in lncRNAs. To address this challenge, we propose DSNm6A, a deep learning framework that learns cross-RNA transferable sequence representations for effective lncRNA m6A site detection. To comprehensively capture patterns and signals of m6A sites, lncRNA and mRNA sequences are first encoded from complementary multiple facets, including One-Hot encoding, nucleotide physicochemical properties and cumulative frequency, and position-specific propensity. Based on these sequence encodings, a domain separation network integrating CNN, Bi-LSTM, and BERT modules is then employed to explicitly disentangle domain-invariant features shared between mRNAs and lncRNAs from their domain-specific counterparts. The shared features are finally fed into a fully connected layer for accurate lncRNA m6A sites prediction. Cross-validation and independent test results demonstrate that DSNm6A consistently outperforms existing methods across nearly all performance metrics, attributed to its superior capacity to learn transferable m6A-related features across RNA types. In addition, DSNm6A exhibits strong robustness and generalization across species.

Cross-resolution person re-identification (CR-ReID) focuses on matching person images of the same identity across different resolutions. Most existing CR-ReID methods rely on manually annotated identity labels for training. Although some researchers have proposed unsupervised CR-ReID (UCR-ReID) methods, the feature fusion techniques they rely on still require a large number of parameters and significant computational resources, limiting the widespread application of UCR-ReID technology. To address the aforementioned issues, we propose an image-text semantic learning (ITSL) method, which incorporates text semantics to enhance recognition performance. During the testing phase, ITSL requires only a single encoder to obtain resolution-invariant features. Specifically, ITSL first learns text features based on a visual-language model, and then utilizes the dual semantic matching module to match inter-resolution positive clusters in both the image and text modalities. During the optimization process, ITSL not only incorporates image semantic contrastive loss to facilitate cross-resolution alignment but also integrates text semantic contrastive loss to leverage text semantics for promoting resolution-invariance learning. Additionally, we design random region downsampling in ITSL, which further enhances the model’s robustness to resolution gaps through data augmentation. Experimental results on multiple cross-resolution datasets show that ITSL not only outperforms existing unsupervised methods while maintaining efficiency, but also approaches the performance of earlier supervised methods on certain datasets.