Accurate segmentation of key anatomical structures in anterior segment OCT (AS-OCT) images is critical for diagnosing serious ophthalmic conditions such as keratitis and cataract. However, due to the scarcity of labeled data in this domain, most existing methods struggle to precisely segment both the lens and the anterior chamber angle simultaneously. To address these limitations, we propose a semi-supervised segmentation framework based on collaborative training between U-Net and Mamba-UNet. A Scale Fusion Module (SFM) is introduced to integrate the outputs of both models, generating multi-scale predictions and fused pseudo-labels. A multi-scale supervision strategy is then employed to guide learning at different levels. Additionally, we design a novel anatomical structure consistency loss that leverages anatomical properties from the fused pseudo-labels to preserve anatomical correctness. Experimental results on two AS-OCT datasets demonstrate the effectiveness and superiority of our proposed approach.

Understanding the associations among microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and various diseases as biomarkers holds significant biological importance. Developing efficient, straightforward prediction models is essential to reduce the high cost of experimental research. However, most existing methods typically predict miRNA-disease associations (MDAs), lncRNA-disease associations (LDAs), and lncRNA miRNA interactions (LMIs) separately, often relying on both similarities and associations as inputs. These approaches complicate their application across diverse biological and medical domains. Moreover, few models are capable of simultaneously predicting all three types of associations in a unified framework. In this work, we propose a novel and simplified model, called Simplified input strategy for Multiple Associations Prediction (SimpleMAP). Unlike previous approaches, SimpleMAP eliminates the need for similarity networks or external biological data and instead uses only known associations as input, reducing feature contamination and ensuring better generalization. SimpleMAP is designed to predict MDAs, LDAs, and LMIs concurrently, by constructing a three-layer heterogeneous biomolecular network that captures the associations among miRNAs, lncRNAs, and diseases. Our method employs a single, end-to-end architecture based on stacked deep matrix factorization (SDMF) to process sparse input data and learn latent features effectively. SimpleMAP is designed to concurrently predict MDAs, LDAs, and LMIs by constructing a three-layer heterogeneous biomolecular network that captures multi-relational associations among miRNAs, lncRNAs, and diseases. To enhance predictive performance, we incorporate multiple feature integration strategies to fuse representations extracted by SDMF. This streamlined design makes SimpleMAP one of the first models to predict multiple bio-entity associations jointly using only minimal input data, offering a highly scalable and biologically meaningful solution. SimpleMAP demonstrates superior performance against strong baselines. Further validation on two additional datasets involving miRNA-circRNA-disease associations confirms the models robustness and adaptability. Finally, biologically validated case studies underscore the realworld applicability of SimpleMAP for biomarker discovery in complex biological systems. Overall, SimpleMAP introduces a new paradigm in bio-entity association predictionłachieving multi-type, high-performance prediction with minimal input complexityłmaking it a valuable tool for computational biology and biomedical research.

Accurate 6D pose estimation of objects during robot grasping is a common task in robotics. It becomes even more challenging when the texture-less objects are situated in a complex environment. We present a part-level 6D pose estimation approach, CPF6D. It takes RGB-D images as input and predicts a 6D pose for each object in the image. This method is capable of integrating the relationships between the object itself and its constituent parts. In CPF6D, a feature extracting module is specifically designed in the encoder layer to enhance the contextual features of the object parts. We use a dual-stream fusion module for each RGB-D image to fuse the RGB and depth data. Also, an optimized strategy specifically for conveyor belt robotic grasping is presented. Experiments on synthetic datasets and in a real-world scenario demonstrate that our method can obtain accurate 6D pose of each object observed and be competitive with other public methods.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Identifying circular RNA (circRNA)-drug sensitivity association (CDsA) is crucial for advancing drug development. As conducting traditional wet experiments for determining CDsA is costly and inefficient, calculation methods have already proven to be a valid approach to cope with this problem. However, there exists limited research addressing the prediction of the CDsA prediction problem, and certain discrepancies persist, particularly concerning false-negative associations. As a consequence, we present a multi-view framework, called MAGSDMF, for identifying latent CDsA. Firstly, MAGSDMF applies ultiple ttention mechanisms and raph learning methods to dynamically extract features and strengthen the features of inside and across multi-similarity networks of circRNA and drug. Secondly, the tack eep atrix Factorization (SDMF) is devised to directly extract features from CDsAs. We consider multi-similarity networks with the original CDsAs as multi-view information. Thirdly, MAGSDMF utilizes a multi-attention channel mechanism to integrate these features for the purpose of reconstructing CDsA. Finally, MAGSDMF performs another DMF based on the reconstruction to identify the latent CDsAs. Simultaneously, contrastive learning (CL) is implemented to enhance the generalization capability of MAGSDMF and oversee the learning process of the underlying links prediction task. In comparative experiments, MAGSDMF achieves superior performance on two datasets with AUC values of 0.9743 and 0.9739 based on 5-fold cross-validation. Moreover, in case studies, the achievements further validate the identification reliability of MAGSDMF.

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.