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.

Session-Based Recommendation (SBR), aiming at predicting user next action in view of anonymous behavior sequences, is a critical task for online services (e.g. e-commerce, streaming media). Recently, Graph Neural Networks (GNN) have achieved significant success in session-based recommendation modeling tasks. However, the graph structure data cannot adequately reflect the sequential information in original sessions, and it is also hard for GNN to capture high-order item relationships. Focusing on the challenges mentioned, we proposed a method called Sequence-enhanced Graph neural networks for Session-Based Recommendation (SG-SBR) to provide an effective solution. First, we introduced positional embedding mechanism to merge sequential information and session representations generated by GNN. Second, we proposed self-attention network to capture both the sequential information and high-order item relationships, which improves the ability to capture sequential information for GNN. The above method not only improves the performance of recommendation, but also can be effortlessly integrated into other session-based recommendation combined with GNN. We conducted extensive experiments on two real datasets, and the results show that SG-SBR outperforms baseline methods consistently.

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.

Deep learning networks (DNNs) have been growing rapidly in recent years, with increasing demands on computing power. Therefore, accelerating the execution of DNN models has become a research hotspot. Operator fusion is a critical optimization strategy to enhance DNN performance in Deep Learning (DL) frameworks, such as TensorFlow, Pytorch, TVM and Halide. However, these frameworks are designed for general optimization and cannot fully harness the specific features of emerging hardware. Moreover, they primarily implement operator fusion at the operator level, missing out on many fusion opportunities and heavily relying on extensive manual optimizations for fused operators. Targeting the Sunway SW26010 Many-Core processor, the basic building block of Sunway TaihuLight supercomputer, we introduce swAutoFuser, an end-to-end automatic operator fusion and code generation framework. swAutoFuser proposes a set of low-level primitives to leverage hardware features and employs an autofuser to achieve primitive level fusion, which breaks operator boundaries and enables more fusion opportunities. In addition, swAutoFuser can automatically generate high-performance fused operator implementations based on a static cost model, significantly reducing the overhead of manually optimizing fused operators. Our experiments demonstrate that swAutoFuser can improve operator performance by 10% to 56%.

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA.

Accumulating evidence indicates that the dysregulation of small nucleolar RNAs (snoRNAs) is relevant with diseases. Identifying snoRNA-disease associations by computational methods is desired for biologists, which can save considerable costs and time compared biological experiments. However, it still faces some challenges as followings: (i) Many snoRNAs are detected in recent years, but only a few snoRNAs have been proved to be associated with diseases; (ii) Computational predictors trained with only a few known snoRNA-disease associations fail to accurately identify the snoRNA-disease associations. In this study, we propose a ranking framework, called iSnoDi-MDRF, to identify potential snoRNA-disease associations based on multiple biological data, which has the following highlights: (i) iSnoDi-MDRF integrates ranking framework, which is not only able to identify potential associations between known snoRNAs and diseases, but also can identify diseases associated with new snoRNAs. (ii) Known gene-disease associations are employed to help train a mature model for predicting snoRNA-disease association. Experimental results illustrate that iSnoDi-MDRF is very suitable for identifying potential snoRNA-disease associations. The web server of iSnoDi-MDRF predictor is freely available at http://bliulab.net/iSnoDi-MDRF/.

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/.

Nextpoint-of-interest (POI) recommendation has received widespread attention in recent years due to its superiority of recommending where users will go to next. However, there exist two limitations in many recommendation methods: (1) the hardness of modeling users' short-term preferences adaptively based on input sequence; (2) the efficient learning of the joint information between users' long- and short-term preferences. To this end, we propose an attention mechanism and adaptive convolution actuated fusion network (AMACF) innovatively, which optimizes forecast effectiveness of user preference. To better model some contextual information such as category, temporal, we utilize long- and short-term memory network to learn contextual features of POIs in historical check-ins and embed self-attention mechanism to capture users' preference in the long-term module. In the short-term module, for capturing the complicated interest, the adaptive convolution network (Ada-CN) is novelly proposed, which applies the attention mechanism to aggregate multiple parallel convolution kernels selectively and adjust to short-term preference according to users' continuously updated check-ins. Furthermore, an attention-based fusion mechanism is designed to combine long- and short-term preferences, where contributions to the next POI of different users are evaluated sufficiently. Experimental results on two real-world data sets indicates that AMACF outperforms baseline methods for next POI recommendation.

Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying missing associations between known piRNAs and diseases. With the fast development of biological experiments, more and more piRNAs are detected. Therefore, the identification of piRNA-disease associations of newly detected piRNAs has significant theoretical value and practical significance on pathogenesis of diseases. In this study, the iPiDA-LTR predictor is proposed to identify associations between piRNAs and diseases based on Learning to Rank. The iPiDA-LTR predictor not only identifies the missing associations between known piRNAs and diseases, but also detects diseases associated with newly detected piRNAs. Experimental results demonstrate that iPiDA-LTR effectively predicts piRNA-disease associations outperforming the other related methods.

BACKGROUND: It's a very urgent task to identify cancer genes that enables us to understand the mechanisms of biochemical processes at a biomolecular level and facilitates the development of bioinformatics. Although a large number of methods have been proposed to identify cancer genes at recent times, the biological data utilized by most of these methods is still quite less, which reflects an insufficient consideration of the relationship between genes and diseases from a variety of factors. RESULTS: In this paper, we propose a two-rounds random walk algorithm to identify cancer genes based on multiple biological data (TRWR-MB), including protein-protein interaction (PPI) network, pathway network, microRNA similarity network, lncRNA similarity network, cancer similarity network and protein complexes. In the first-round random walk, all cancer nodes, cancer-related genes, cancer-related microRNAs and cancer-related lncRNAs, being associated with all the cancer, are used as seed nodes, and then a random walker walks on a quadruple layer heterogeneous network constructed by multiple biological data. The first-round random walk aims to select the top score k of potential cancer genes. Then in the second-round random walk, genes, microRNAs and lncRNAs, being associated with a certain special cancer in corresponding cancer class, are regarded as seed nodes, and then the walker walks on a new quadruple layer heterogeneous network constructed by lncRNAs, microRNAs, cancer and selected potential cancer genes. After the above walks finish, we combine the results of two-rounds RWR as ranking score for experimental analysis. As a result, a higher value of area under the receiver operating characteristic curve (AUC) is obtained. Besides, cases studies for identifying new cancer genes are performed in corresponding section. CONCLUSION: In summary, TRWR-MB integrates multiple biological data to identify cancer genes by analyzing the relationship between genes and cancer from a variety of biological molecular perspective.

We combine DEM, land-use, soil, weather etc to drive the SWAT model to simulate 1992-2001 yearly and monthly surface runoff in Songhuaba water source conservation region. According to model calibration, 4 kinds of LUCC scenarios are set to predict the Changes of runoff. The conclusions are as follows: (2)The simulated results show that surface runoff can increase most in S1 (a non-vegetation area), which following by seriously soil and water loss. In S2(farmlands, garden plots becoming woodlands). S3 (habitations, industrial and mining areas, garden plots turning into woodlands) and S4(only woodlands)for closing hill for forestation in different level, S3 can increase surface runoff more obviously. (2)In a long run, moving the people to other places is more benefit for protecting the water than making them engaging other work.

The concentration of Cu, Ni, Cr, Zn, Pb and As in urban street dust collected from Kunming were analyzed by using X-Ray fluorescence spectrometry. Spatial technique was applied to study the content level and the spatial distribution character of heavy metal pollution in street dust of Kunming. The ecological risks of heavy metals in street dust were assessed by the potential ecological risk index (ERI) method. The results show that the average concentrations of Cu, Ni, Cr, Zn, Pb and As were much higher than the background concentrations of soil in China, in which Cu, Zn, Pb and As have reached the relative enrichment classes, these four elements heavier pollution. Accumulation of heavy metals in the dry season was significantly higher than during the rainy season. The highest content of heavy metals was in the industrial areas, the followed were the traffic areas. Heavy metals mainly came from traffic pollution in urban street dust and industrial pollution in Suburbs. The potential ecological risk level of heavy metals in street dust of Kunming reached the lightly ecological risk levels.

Based on the analyses results of the major and trace elements of the acid soluble (AS) and insoluble (AI) fractions of the Shell Bar section from the Qaidam Basin(QB) in the NE Tibetan Plateau(TP), and the correlations between the related elements and their ratios, the depositional environment of the section was discussed. The results show that the major and trace elements and their ratios of AS are good proxies of the salinity, redox condition and the temperature of lake, and the elements and their ratios of AI were related with source material and chemical weathering. According to these proxies and analyses results, we reconstructed the paleoclimate and water level fluctuation history during the high lake level period lasting between 43.5 and 22.4 cal. ka BP.