MOTIVATION: Identifying non-coding RNAs (ncRNAs) associated with drug resistance is critical for elucidating molecular mechanisms underlying drug response, facilitating drug screening, and discovering novel therapeutic targets. While several graph neural network-based methods have been proposed to infer ncRNA-drug resistance associations, they remain fundamentally constrained by semantic distortion induced by a sparse bipartite network and neglect of relational semantics among molecular entities, ultimately compromising both predictive reliability and biological interpretability. RESULTS: In this study, we propose iNcRD-HG, a novel framework for identifying ncRNA-drug resistance associations. The framework addresses three critical aspects: constructing a context-enriched heterogeneous network that integrates six distinct molecular interaction types with bio-entity-specific attributes, developing a semantic-enhanced graph learning architecture that implements relation-type-aware message passing to capture complex contextual dependencies, and introducing an interpretability mechanism to reveal potential synergistic pathways underlying drug response. Experimental results demonstrate that iNcRD-HG achieves superior predictive performance across diverse benchmark datasets while deriving association features with strong discriminative capability. By identifying molecular synergistic contexts, iNcRD-HG provides mechanistically interpretable insights into ncRNA-mediated drug resistance. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/iNcRD-HG.

The acquisition of expert-annotated data remains a critical bottleneck for medical image segmentation, thereby constraining the clinical applicability of highly accurate models. Crucially, despite this scarcity of labeled data, the intrinsic homogeneity in human anatomy across the cohort provides a fundamental basis (or: a promising leverage point) for enhancing model generalization and training efficiency by exploiting inter-instance anatomical complementarity. In this study, we propose a novel semi-supervised approach for medical image segmentation that fully exploits this inter-instance complementarity. The proposed model operates at two levels, integrating a sophisticated copy-paste augmentation module (CPAM) and a trainable region calibration mechanism (TRCM) within the simple mean teacher (MT) framework. Specifically, CPAM is a carefully designed copy-paste strategy that facilitates the exchange of informative regions between samples, thereby enhancing the diversity and robustness of the training data. TRCM leverages the predictions from labeled regions to guide and calibrate the trainable regions in unlabeled data. The calibrated regions typically yield high-quality pseudo-labels, which effectively improve model training. CPAM and TRCM work synergistically, complementing each other to enhance model performance. Experiments on diverse medical image datasets-including LA, ACDC, BraTS2019, and Pancreas-NIH-covering both MRI and CT modalities demonstrate the robust efficacy of our proposed model. In settings with limited annotated data, the model consistently outperforms current state-of-the-art methods across multiple evaluation metrics. The code is available at https://github.com/shuaiaihang/shuaiAIMedcalLab.

An effective method for improving the object detection performance is to decrease the number of false positive (NFP) detection boxes and increase the number of true positive (NTP) detection boxes. In terms of the region-based object detection framework, an appropriate sample weighting strategy can help effectively achieve this goal without causing any inference efficiency loss. However, designing a suitable weighting method is not easy, and a reasonable guiding metric and comprehensive analysis are needed. This article directly sets the NFP and NTP as the evaluation metrics and examines how some preliminary weighting methods affect these two metrics. Based on the results of our analysis, we carefully design a simple yet effective sample weighting method, referred to as the interval normalization weighting strategy (INWS). Unlike some previous works, which only view sample losses as the weighting factor (e.g., focal losses), the INWS applies both the foreground score and the intersection over union (IoU) as the weighting factors. The INWS consists of two components: the IoU interval score normalization strategy (IISNS) for negative samples and the score interval IoU normalization strategy (SIINS) for positive samples. The IISNS can effectively decrease the NFP, and the SIINS is beneficial for increasing the NTP, especially under higher IoU thresholds. Furthermore, the INWS is convenient for application to most of the existing region-based object detection models. The experimental results on the mainstream benchmarks demonstrate that our INWS can achieve consistent improvements on various baselines.

Protein-RNA interactions (PRIs) play pivotal roles in biological processes such as gene regulation, making their prediction essential for therapeutic and mechanistic studies. While traditional wet-lab methods are timeconsuming and challenging, computational approaches offer efficient alternatives. Graph-based methods show promise by capturing both direct interactive domains (protein-RNA interaction) and indirect collaborative domains (functional similarity among proteins/RNAs). However, integrating these domains and learning meaningful node representations remain critical challenges. To address this, we propose CFPLM, a collaborative framework fusing large language models, graph convolutional networks, and cross-attention mechanisms to improve PRI prediction. Experiment results demonstrate that CFPLM achieves robust, state-of-the-art performance across three benchmark datasets. It's anticipated to have applicability to similar other interaction prediction tasks. The data and codes are available at: https://github.com/HuanchaoFeng/CFPLM.

Identification of intrinsically disordered regions (IDRs) in proteins is essential for understanding fundamental cellular processes. The IDRs can be divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their lengths. In previous studies, most computational methods ignored the differences between LDRs and SDRs, and therefore failed to capture the different patterns of LDRs and SDRs. In this study, we propose IDP-EDL, an ensemble of three predictors. The component predictors were first built based on pretrained protein language model and applied task-specific fine-tuning for short, long, and generic disordered regions. A meta predictor was then trained to integrate three task-specific predictors into the final predictor. The results of experiments show that task-specific supervised fine-tuning can capture the different features of LDRs and SDRs and IDP-EDL can achieve stable performance on datasets with different ratios of LDRs and SDRs. More importantly, IDP-EDL can reach or even surpass state-of-the-art performance than other existing predictors on independent test sets. IDP-EDL is available at https://github.com/joestarXjx/IDP-EDL.

Exploring disease mechanisms at the lncRNA level provides valuable guidance for disease prognosis and treatment. Recently, there has been a surge of interest in exploring disease mechanisms via computational methods to overcome the challenge of tremendous manpower and material resources in biological experiments. However, current computational methods suffer from two main limitations: simple data structures that do not consider the close association between multiple types of data, and the lack of a systematic pathogenesis analysis that identified disease-associated lncRNAs are not applied to the downstream disease prognosis and therapeutic analysis from the perspective of data analysis. In this end, we present a systemic pipeline including disease-associated lncRNAs identification and downstream pathogenesis analysis on how the predicted lncRNAs are involved in the disease prognosis and therapy. Due to the importance of identifying disease-associated lncRNAs and the weak interpretability of existing computational identification methods, we propose a novel approach named iLncDA-PT to identify disease-associated lncRNAs considering the interactions between various bio-entities outperforming the other state-of-the-art methods, and then we conduct a systematically subsequent analysis on prognosis and therapy for a specific disease, hepatocellular carcinoma (HCC), as an example. Finally, we reveal a significant association between immune checkpoint expression, tumor microenvironment, and drug treatment.

MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWGCN is proposed for piRNA-disease association prediction. There are three main contributions of iPiDA-SWGCN: (i) Potential piRNA-disease associations are preliminarily supplemented in the sparse piRNA-disease network by integrating various basic predictors to enrich network structure information. (ii) The original Boolean piRNA-disease associations are assigned with different relevance confidence to learn node representations from neighbour nodes in varying degrees. (iii) The experimental results show that iPiDA-SWGCN achieves the best performance compared with the other state-of-the-art methods, and can predict new piRNA-disease associations.

Circular RNAs (circRNAs) are severing as important regulators for various physiological and pathological life activities. Identifying associations between circRNAs and diseases can help uncover the disease mechanism, and promote the diagnosis and treatment of human diseases. To provide assisting guidance and optimize biological experiments, some computational methods have been proposed to predict circRNA-disease associations. However, most predictors focus on identifying missing associations for known circRNA and diseases. It is still challenging to effectively detect potential circRNA-disease association pattern because of their limited generation ability and insufficient pair representation. In this regard, we propose a novel computational method named iCircDA-ENR for identifying circRNA-disease associations based on ensemble network representation. Different from other predictors, iCircDA-ENR is a ranking method. Multiple biological information and meta-paths are introduced to construct heterogeneous relation network, and then different network representation algorithms are incorporated into ranking framework to capture informative network features. The learned ranking predictor prioritizes the candidate diseases for query circRNAs according to their relevance degree. Experimental results illustrate that iCircDA-ENR achieves better performance and wider applicability, benefited from its sufficient representation and effective learning.

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.

MOTIVATION: Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. RESULTS: With the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.

Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

MOTIVATION: As one of the most important and widely used mainstream iterative search tool for protein sequence search, an accurate Position-Specific Scoring Matrix (PSSM) is the key of PSI-BLAST. However, PSSMs containing non-homologous information obviously reduce the performance of PSI-BLAST for protein remote homology. RESULTS: To further study this problem, we summarize three types of Incorrectly Selected Homology (ISH) errors in PSSMs. A new search tool Supervised-Manner-based Iterative BLAST (SMI-BLAST) is proposed based on PSI-BLAST for solving these errors. SMI-BLAST obviously outperforms PSI-BLAST on the Structural Classification of Proteins-extended (SCOPe) dataset. Compared with PSI-BLAST on the ISH error subsets of SCOPe dataset, SMI-BLAST detects 1.6-2.87 folds more remote homologous sequences, and outperforms PSI-BLAST by 35.66% in terms of ROC1 scores. Furthermore, this framework is applied to JackHMMER, DELTA-BLAST and PSI-BLASTexB, and their performance is further improved. AVAILABILITY AND IMPLEMENTATION: User-friendly webservers for SMI-BLAST, JackHMMER, DELTA-BLAST and PSI-BLASTexB are established at http://bliulab.net/SMI-BLAST/, by which the users can easily get the results without the need to go through the mathematical details. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA-disease associations are highly required. The existing predictors consider circRNA-disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task. RESULTS: In this study, we proposed a new predictor called iCricDA-LTR for circRNA-disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner. The experimental results on two independent test datasets showed that iCircDA-LTR outperformed the other competing methods, especially for predicting the diseases associated with new circRNAs. As a result, iCircDA-LTR is more suitable for the real-world applications. AVAILABILITY AND IMPLEMENTATION: For the convenience of researchers to detect new circRNA-disease associations. The web server of iCircDA-LTR was established and freely available at http://bliulab.net/iCircDA-LTR/.

Identification of lncRNA-disease associations is not only important for exploring the disease mechanism, but will also facilitate the molecular targeting drug discovery. Fusing multiple biological information is able to generate a more comprehensive view of lncRNA-disease association feature. However, the existing fusion strategies in this field fail to remove the noisy and irrelevant information from each data source. As a result, their predictive performance is still too low to be applied to real world applications. In this regard, a novel computational predictor called iLncRNAdis-FB is proposed based on the Convolution Neural Network (CNN) to integrate different data sources by using the feature blocks in a supervised manner. The lncRNA similarity matrix and disease similarity matrix are constructed, based on which the three-dimensional feature blocks are generated. These feature blocks are then fed into CNN to train the model so as to predict unknown lncRNA-disease associations. Experimental results show that iLncRNAdis-FB achieves better performance compared with other state-of-the-art predictors. Furthermore, a web server of iLncRNAdis-FB has been established at http://bliulab.net/iLncRNAdis-FB/, by which users can submit lncRNA sequences to detect their potential associated diseases.

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.