Methods最新文献

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vital for advancing diagnostic, preventive, and therapeutic strategies. Existing computational methods often address lncRNA-miRNA-disease associations as isolated tasks, resulting in sparse connections and limited generalizability. Additionally, these ncRNA-disease relationships involve higher-order topological information that is frequently overlooked. To address these challenges, we propose the MTL-NRDA model, which employs a multi-task learning framework to simultaneously predict lncRNA-disease associations, miRNA-disease associations, and lncRNA-miRNA interactions. The model integrates multi-source information through a heterogeneous network encompassing lncRNAs, miRNAs, and disease association networks as well as various similarity networks. Node embeddings are optimized by combining local and global contexts, and local features are aggregated using higher-order graph convolutional networks (HOGCN) to capture ncRNA-disease associations, while global features are extracted via a transformer encoder, effectively handling long-range dependencies. MTL-NRDA uses independent bilinear output layers for each task and dynamically adjusts the loss weights to calculate task-specific association probabilities. Experiments on two independent datasets show that MTL-NRDA outperforms existing models. Ablation studies confirmed the effectiveness of the model components and multi-task strategy, whereas hyperparameter tuning further improved the performance. Case studies on breast and liver cancers demonstrated the practical applicability of the model.

Efficiently identifying cancer driver genes plays a key role in the cancer development, diagnosis and treatment. Current unsupervised driver gene identification methods typically integrate multi-omics data into gene function networks and employ network embedding algorithms to learn gene features. Additionally, they consider mutual exclusivity and mutation frequency as crucial concepts in identifying driver genes. However, existing approaches neglect the possible important implications of mutual exclusivity in the embedding space. Furthermore, they simply assume that all driver genes exhibit high mutation frequencies. Fortunately, we explored the mutual exclusivity implanted in the learned features and have verified that the Euclidean distances between learned features are strongly related to the mutual exclusivity and they can reveal more information for the mutual exclusivity. Thus, we designed an unsupervised driver gene predicting framework DriverMEDS based on the above idea and a novel driver mutation scoring strategy. First, we design a feature clustering algorithm to generate gene modules. In each module, the Euclidean distances of learned features are used to calculate a module importance score for each gene based on the related mutual exclusivity. Then, following the fact that most of driver genes have intermediate mutation frequencies, a driver mutation scoring function is designed for each gene to optimize the existing mutation frequency scoring strategy. Finally, the weighted sum of the module importance score and the driver mutation score is used to prioritize the genes. The experiment results and analysis show that DriverMEDS could detect novel cancer driver genes and relevant function modules, and outperforms other five state-of-the-art methods for cancer driver identification.

In recent years, RNA transcriptome sequencing technology has been continuously evolving, ranging from single-cell transcriptomics to spatial transcriptomics. Although these technologies are all based on RNA sequencing, each sequencing technology has its own unique characteristics, and there is an urgent need to develop an algorithmic toolkit that integrates both sequencing techniques. To address this, we have developed OmniClust, a toolkit based on single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. OmniClust employs deep learning algorithms for feature learning and clustering of spatial transcriptomics data, while utilizing machine learning algorithms for clustering scRNA-seq data. OmniClust was tested on 12 spatial transcriptomics benchmark datasets, demonstrating high clustering accuracy across multiple clustering evaluation metrics. It was also evaluated on four scRNA-seq benchmark datasets, achieving high clustering accuracy based on various clustering evaluation metrics. Furthermore, we applied OmniClust to downstream analyses of spatial transcriptomics and single-cell RNA breast cancer data, showcasing its potential to uncover and interpret the biological significance of cancer transcriptome data. In summary, OmniClust is a clustering tool designed for both single-cell transcriptomics and spatial transcriptomics data, demonstrating outstanding performance.