Currently, an increasing number of researchers are focusing on partial multiview incomplete multilabel learning. However, many methods generally integrate features from multiple views via an average weighting strategy, which overlooks the potential mismatch between the contribution of each view and their assigned fusion weights and thus generates unreliable fused features. To address this issue, we propose a novel uncertainty-driven reliable dynamic fusion framework for partial multiview incomplete multilabel learning. Unlike existing methods, the proposed uncertainty-driven reliable sample-level dynamic fusion module operates on the principle that samples exhibiting greater uncertainty possess fewer reliable features. This module evaluates the uncertainty of each sample and, in turn, estimates the reliability of features with the uncertainty of sample judgement, thereby obtaining reliable weights to guide the information fusion of multiple views. Furthermore, many existing approaches for handling incomplete multilabel scenarios typically concentrate on the information from annotated labels, neglecting the potential information of unknown tags. To bridge this gap, we incorporate an innovative pseudolabelling strategy that effectively identifies trustworthy pseudolabels that correspond to those unannotated uncertain labels, thereby adding additional supervisory information to assist model training. Moreover, we also devise a feature masking strategy to further augment the encoder's representation learning capabilities. The experimental results across five datasets demonstrate that our method outperforms current state-of-the-art methods.

As a prominent research topic, multi-view multi-label classification (MvMlC) aims to assign multiple labels to samples by integrating information from various perspectives. However, in real-world scenarios, MvMlC frequently faces the learning challenge of data with missing views and labels, typically resulting from sensor malfunctions, or the costly and time-consuming process of manual annotation. In addition, learning robust representations that are both consistent across views and specific to individual views remains a challenge. To address these issues, we propose a novel double incomplete multi-view multi-label classification framework based on Disentangling Consistent and Specific Information (DCSI). Specifically, we employ a dual-channel encoder with identical architecture but distinct objectives to extract cross-view consistent information and view-specific unique information from all views, respectively. Meanwhile, a view discriminator is constructed to decouple these two types of information, facilitating the extraction of pure consistent and specific information. Moreover, we meticulously design fusion strategies tailored to each representation type. Regarding consistent representations, we propose a dynamic-confidence-aware fusion mechanism that assesses the reliability of each view's representations in relation to the classification task, enabling the model to prioritize information from trustworthy representations. For specific representations, in light of their complementary rather than redundant property, we suggest treating such representations from each view equally to ensure fairness. Through experimental validation on five datasets, the results demonstrate that our method outperforms existing state-of-the-art methods.

Spatial Transcriptomics offers unprecedented opportunities to explore tissue architecture by capturing gene expression with spatial context. However, effectively learning discriminative and spatially smooth representations for accurate spatial domain identification remains a significant challenge. To address this, we propose CSMVL, a multi-view representation learning framework to learn high-quality spot representations by synergistically enhancing both discriminability and spatial continuity. CSMVL introduces a cluster structure learning strategy that guides cell representations within the same domain toward their cluster center while simultaneously separating distinct cluster centers, thereby improving intra-domain compactness and inter-domain separability. Furthermore, graph smoothness regularization is introduced to ensure that representations of spatially adjacent cells within the same domain transition smoothly, reflecting the inherent spatial continuity of biological tissues. Extensive experiments on public ST datasets demonstrate CSMVL's superiority, achieving an average ARI of 71.64% and NMI of 73.43%, outperforming existing state-of-the-art methods

Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, often resulting from data collection oversights and uncertainties in manual annotation. Furthermore, the task of learning robust multi-view representations that are both view-consistent and view-specific from diverse views still a challenge problem in MvMLC. To address these issues, we propose a novel framework for incomplete multi-view multi-label classification (iMvMLC). Our method factorizes multi-view representations into two independent sets of factors: view-consistent and view-specific, and we correspondingly design a graph disentangling loss to fully reduce redundancy between these representations. Additionally, our framework innovatively decomposes consistent representation learning into three key sub-objectives: (i) how to extract view-shared information across different views, (ii) how to eliminate intra-view redundancy in consistent representations, and (iii) how to preserve task-relevant information. To this end, we design a robust task-relevant consistency learning module that collaboratively learns high-quality consistent representations, leveraging a masked cross-view prediction (MCP) strategy and information theory. Notably, all modules in our framework are developed to function effectively under conditions of incomplete views and labels, making our method adaptable to various multi-view and multi-label datasets. Extensive experiments on five datasets demonstrate that our method outperforms other leading approaches.

Historical information is crucial for resolving the challenges of the target state changes. Plenty of methods following memory network, which map the historical data and search areas into a unified embedding space to solve feature misalignment, have attained great performance in visual tracking. However, single-scale feature fusion of historical prompts and spatial features, without fully utilizing the rich information embedded in the prompt, struggles to handle complex scenarios such as low resolution and fast motion. To address the above problem, we propose the Adaptive Spatio-Temporal Information Fusion Module, which dynamically integrates spatio-temporal information from various embedding spaces during the inference process of each frame, unlocking the potential of the prompt. Additionally, to alleviate the issues of semantic bias in historical prompts, we propose Adaptive Triplet Attention to refine historical data through cross-dimensional interactions between channels. In the end, we built a novel tracker called ASTrack, which adaptively generates the task-relevant discriminative features through the fusion of spatio-temporal features. Our method is evaluated on six tracking benchmarks, and the results confirm the effectiveness and robustness of the proposed approach.

Recently, the topic of multi-view multi-label classification has aroused significant attention from scholars. Plenty of methods adopt an average weighting scheme to merge the features obtained from multiple views, which commonly ignore the quality difference of information provided by multiple views and thus limit the credibility of the fusion feature for the overall task. Besides, most of these methods assume the views and labels are complete while neglecting both views and labels may be incomplete. To solve these problems, we propose a quality-aware representation fusion network for partial multi-view incomplete multi-label classification, named QARF-net. Since assigning equal fusion weights for each view may be not in line with the actual contributions of individual views, a view quality-aware module is proposed to learn suitable weights for different views dynamically based on the quality of each view’s information, which provides a reliable guide for fusing the information of multiple views. In addition, considering the consistency characteristics of multi-view data, we impose a sample-level dual constraint to preserve the consistency property of the feature in multi-view space and constrain the sample structure in the fused feature space, respectively. Last but not least, QARF-net can not only deal with complete multi-view multi-label classification tasks but also tackle partial multi-view incomplete multi-label classification tasks. Experimental results on five real-world datasets indicate that our proposed method outperforms state-of-the-art methods.

MOTIVATION: Molecular profiling of blood-based liquid biopsies is a promising disease detection method, which overcomes the limitations of invasive diagnostic strategies. Recently, gene expression profiling of platelets reportedly provides valuable resource for developing new biomarkers for the detection of diseases, including cancer. However, there is no database containing RNAs in platelets. RESULTS: In this study, we constructed PltDB (http://www.pltdb-hust.com), a blood platelets-based gene expression database featuring integration and visualization of RNA expression profiles based on RNA-seq and microarray data spanning both normal individuals and patients with different diseases. PltDB currently contains the expression landscape of mRNAs, lncRNAs, circRNAs and miRNAs in platelets from patients with different disease types and healthy controls. Moreover, PltDB provides users with the tools for visualizing results of comparison and correlation analysis and for downloading expression profiles and analysis results. A submission interface for the scientific community is also embraced for uploading novel RNA expression profiles derived from platelet samples. PltDB will offer a comprehensive review of the clinical use of platelets, overcome technical problems when analyzing data from diverse studies and serve as a powerful platform for developing new blood biomarkers. AVAILABILITY AND IMPLEMENTATION: PltDB is accessible at http://www.pltdb-hust.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.