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Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition. Author summary: Spatial transcriptome sequencing can not only reveal the mechanisms of disease development, but can also be used to explore the structure of biological tissues, which are widely used in a variety of fields such as developmental biology and oncology. To utilize spatial transcriptome data to understand how different cells work together to perform complex functions, spatially relevant groups of cells must be identified, which leads to the task of dissecting tissues into spatial domains. However, most of the currently available spatial domain identification tools have yet to fully utilize the information embedded in spatial transcriptomic data, and their identification remains to be improved for the fine-grained exploration of tissue structure. Here, we developed a tool for accurate spatial domain identification, and our tool makes full use of the various types of spatial transcriptome data from multiple perspectives, identifying the real grouping information in spatial transcriptome data as much as possible. Our results show that our spatial domain identification tool can identify spatial domains more accurately and provide effective help for biological organization structure exploration. [ABSTRACT FROM AUTHOR]

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field. Author summary: In the field of gene editing, the application of deep learning technologies holds significant promise for predicting off-target effects in the CRISPR-Cas9 system. Nevertheless, one of the primary challenges encountered is the extreme imbalance among classes within the off-target datasets, which severely hampers the predictive accuracy for certain classes. Furthermore, as an array of sequence encoding methods continue to evolve, there has been a corresponding increase in model complexity. Addressing these issues, we introduce a novel Efficiency and Specificity-Based (ESB) class rebalancing strategy designed to mitigate the impact of class imbalance. Additionally, we assess the influence of six encoding schemes and four distinct architectural approaches on the prediction performance, employing four benchmark datasets for validation. Building upon these insights, we have developed a new hybrid model, termed CRISPR-MCA. Our experimental results demonstrate that the ESB strategy significantly surpasses the performance of existing baseline methods across multiple models. Moreover, the CRISPR-MCA model exhibits robust performance on two distinct types of datasets, affirming its effectiveness in enhancing the accuracy of deep learning predictions for off-target activities. [ABSTRACT FROM AUTHOR]

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification. Author summary: Cancer has become a major disease threatening human life and health. Cancer usually originates from abnormal gene activities, such as mutations and copy number variations. Mutations in cancer driver genes are crucial for the selective growth of tumor cells. Identifying cancer driver genes is crucial in cancer-related research and treatment strategies, as it helps understand cancer occurrence and development. However, the complex gene-gene interactions, measurement errors, and the prevalence of unlabeled data significantly complicate the identification of these driver genes. We developed a new method that integrates an energy-constrained diffusion mechanism with an attention mechanism to uncover implicit gene dependencies in biomolecular networks and generate robust gene representations. Extensive experiments demonstrated that our model accurately identifies known cancer driver genes and effectively discovers potential ones. Furthermore, we analyzed and predicted patient-specific mutated genes, enhancing our understanding of their pathogenesis and advancing precision medicine. In summary, our method offers a promising tool for advancing the identification of cancer driver genes. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data. Author summary: It is still difficult to analyze scRNA-seq data because a significant portion of expressed genes have zeros. Gene expression levels can be restored through the imputation of scRNA-seq data, facilitating downstream analysis. To overcome this challenge, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. Through the use of several simulated and real datasets, we perform an comprehensively evaluation of scRNMF against existing methods. scRNMF can enhance various aspects of downstream analysis, including gene expression data recovery, cell clustering analysis, gene differential expression analysis, and cellular trajectory reconstruction. The results of our study demonstrate that scRNMF is a powerful tool that can improve the accuracy of single-cell data analysis. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA–drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub: https://github.com/ZZCrazy00/GAM-MDR. [ABSTRACT FROM AUTHOR]

Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose. [ABSTRACT FROM AUTHOR]

AIM: The study aimed to explore the effect of curcumin (Cur) on lipopolysaccharide (LPS)-induced RAW264. 7 cell-derived osteoclasts, together with its underlying mechanism. METHODS: An osteoclast model was established by treating RAW264. 7 cells with LPS. The viability of the cells was assessed by CCK-8 assays and osteoclast formation was evaluated by tartrate-resistant acid phosphatase (TRAP) activity. The levels of reactive oxygen species (ROS), Fe2+, glutathione (GSH), and malondialdehyde (MDA) were examined by biochemical assays. Mitochondrial morphology was assessed by transmission electron microscopy. The mRNA and protein levels of p53, glutathione peroxidase 4 (GPX4), and solute carrier family 7 member 11 (SLC7A11) were determined by RT-qPCR and Western blot, respectively. RESULTS: Treatment with LPS successfully induced osteoclasts formation in RAW264. 7 cells. The TRAP results showed that compared with the LPS-treated group, the number of osteoclasts and TRAP activity in the curcumin-treated group decreased dose-dependently (P<0. 01). Compared with the control group, the LPS+Erastin group showed significantly increased TRAP activity (P<0. 01), while after curcumin treatment, the TRAP activity declined in a dose-dependent manner (P<0. 01). The results of the biochemical tests showed that compared with the control group, the LPS+Erastin group had significantly elevated levels of ROS, Fe2+, and MDA, while the GSH level was significantly reduced (P< 0. 01, and compared with the LPS+Erastin group, the ROS, Fe2+, and MDA levels in the curcumin group decreased (P< 0. 01) and GSH levels increased (P<0. 01). These effects were all dose-dependent. Transmission electron microscopy showed that compared with the LPS group, the LPS+Erastin group had reduced mitochondrial cristae and increased membrane density, while after treatment with curcumin, both these effects were reversed. The RT-qPCR and Western blot results showed that compared with the control group, the mRNA and protein levels of p53 in the LPS+Erastin group were significantly increased, while those of of SLC7A11 and GPX4 were significantly reduced (P<0. 01). After curcumin treatment, the p53 mRNA and protein levels were reduced while the levels of SLC7A11 and GPX4 were increased (P<0. 01). CONCLUSION: Curcumin can inhibit lipopolysaccharide-induced differentiation of RAW264. 7 cells into osteoclasts, and its mechanism may be related to the inhibition of the p53/SLC7A1 1/GPX4 signaling pathway. [ABSTRACT FROM AUTHOR]

Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks, a form of deep learning technology, accurately model the structure of compound molecules, enhancing predictions of their ADT. However, many existing methods emphasize atom-level information transfer and overlook crucial data conveyed by molecular bonds and their interrelationships. Additionally, these methods often generate "equal" node representations across the entire graph, failing to accentuate "important" substructures like functional groups, pharmacophores, and toxicophores, thereby reducing interpretability. Results We introduce a novel model, GraphADT, utilizing structure remapping and multi-view graph pooling (MVPool) technologies to accurately predict compound ADT. Initially, our model applies structure remapping to better delineate bonds, transforming "bonds" into new nodes and "bond-atom-bond" interactions into new edges, thereby reconstructing the compound molecular graph. Subsequently, we use MVPool to amalgamate data from various perspectives, minimizing biases inherent to single-view analyses. Following this, the model generates a robust node ranking collaboratively, emphasizing critical nodes or substructures to enhance model interpretability. Lastly, we apply a graph comparison learning strategy to train both the original and structure remapped molecular graphs, deriving the final molecular representation. Experimental results on public datasets indicate that the GraphADT model outperforms existing state-of-the-art models. The GraphADT model has been demonstrated to effectively predict compound ADT, offering potential guidance for the development of contact drugs and related treatments. Availability and implementation Our code and data are accessible at: https://github.com/mxqmxqmxq/GraphADT.git. [ABSTRACT FROM AUTHOR]