MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction

Adaptive Meta-Path Selection Based Heterogeneous Spatial Enhancement for circRNA-Disease Associations Prediction

Circular RNAs (circRNAs) play a crucial role in human biological processes as miRNA sponges, regulating gene expression and affecting disease manifestations. Establishing heterogeneous nodal feature relationships through meta-paths can effectively enhance the predictive capability of models. Previous research primarily constructed associations between meta-paths manually, and excessive noise made it difficult to capture highly correlated hidden features. Equally important is learning more about feature distributions, which is key to improving the generalization ability of algorithms. To address these challenges, we propose an adaptive meta-path selection method named AdaMH. The core scheme introduces an adaptive path selection method that automatically identifies highly relevant heterogeneous meta-paths during iterative training rounds. Considering the sparsity of data distribution, we introduce controlled random noise into the data through graph contrastive learning to ensure an even distribution of features. Subsequently, a multi-head attention mechanism is utilized to capture relationships in the high-dimensional heterogeneous feature space, enhancing feature representation capability. Comparing with state-of-the-art (SOTA) algorithms, AdaMH is the only one that surpasses a performance threshold of 0.95 across seven evaluation metrics.

Exploring the Role of Ferroptosis-Related Circular RNAs in Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular event associated with high mortality and significant morbidity. Recent studies have highlighted the emerging role of ferroptosis, a novel form of regulated cell death, in the pathogenesis of SAH. Circular RNAs (circRNAs), have been found to play essential roles in various cellular processes, including gene regulation and disease pathogenesis. The expression profile of circRNAs in neural tissues, particularly in the brain, suggests their critical role in synaptic function and neurogenesis. Moreover, the interplay between circRNAs and ferroptosis-related pathways, such as iron metabolism and lipid peroxidation, is explored in the context of SAH. Understanding the functional roles of specific circRNAs in the context of SAH may provide potential therapeutic targets to attenuate ferroptosis-associated brain injury. Furthermore, the potential of circRNAs as diagnostic biomarkers for SAH severity, prognosis, and treatment response is discussed. Overall, this review highlights the significance of studying the intricate interplay between circRNAs and ferroptosis in the context of SAH. Unraveling the mechanisms by which circRNAs modulate ferroptotic cell death may pave the way for the development of novel therapeutic strategies and diagnostic approaches for SAH management, ultimately improving patient outcomes and quality of life.

Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms

MOTIVATION

Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance.

RESULTS

This study presents a method that combines multi-view shared units and multichannel attention mechanisms to predict circRNA-disease associations (MSMCDA). MSMCDA first constructs similarity and meta-path networks for circRNAs and diseases by introducing shared units to facilitate interactive learning across distinct network features. Subsequently, multichannel attention mechanisms were used to optimize the weights within similarity networks. Finally, contrastive learning strengthened the similarity features. Experiments on five public datasets demonstrated that MSMCDA significantly outperformed other baseline methods. Additionally, case studies on colorectal cancer, gastric cancer, and nonsmall cell lung cancer confirmed the effectiveness of MSMCDA in uncovering new associations.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/zhangxue2115/MSMCDA.git.

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset

Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) models have been designed to detect diabetes, many exhibit unsatisfactory performance, are not publicly available, and lack validation on external datasets. To address these limitations, we have developed REMED-T2D, an advanced ensemble ML approach that enhances predictive accuracy and robustness through the integration of diverse ML algorithms. Our approach involves a rigorous data preprocessing process and systematic evaluation of 20 different algorithms, encompassing both conventional ML and deep learning for diabetes prediction. Firstly, we applied an under-sampling approach to an imbalanced Pima Indian Diabetes dataset and generated five balanced datasets. Using these datasets, we investigated various computational strategies to select the optimal model for accurate diabetes classification. Our results demonstrate that REMED-T2D outperformed state-of-the-art methods on the training dataset, with notable improvements in ACC (1.40-4.60%) and MCC (3.50-9.80%). Extensive external validations revealed that the model trained on a five-feature subset achieved ACC of 92.61 % and 92.26 % on the RTML1 and Pabna datasets, respectively. Moreover, a model based on a seven-feature subset improved ACC by 2.80 % and MCC by 13.27 % on the RTML2 dataset. These results suggest the potential of REMED-T2D to predict diabetes in Asian females. Notably, this is the first study to conduct such a comprehensive analysis using the Pima dataset, incorporating a diverse set of ML algorithms. Furthermore, we have developed a publicly accessible web server (https://balalab-skku.org/REMED-T2D/) to facilitate self-monitoring and timely medical interventions. We believe REMED-T2D will assist healthcare professionals in detecting diabetes earlier and implementing preventive measures, ultimately improving health outcomes for those at risk.

CSGDN: contrastive signed graph diffusion network for predicting crop gene-phenotype associations

Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulation activity of specific genes will be adjusted accordingly in different cell types, developmental timepoints, and physiological states. There are the following two problems in obtaining the positive/negative associations between gene and phenotype: (1) high-throughput DNA/RNA sequencing and phenotyping are expensive and time-consuming due to the need to process large sample sizes; (2) experiments introduce both random and systematic errors, and, meanwhile, calculations or predictions using software or models may produce noise. To address these two issues, we propose a Contrastive Signed Graph Diffusion Network, CSGDN, to learn robust node representations with fewer training samples to achieve higher link prediction accuracy. CSGDN uses a signed graph diffusion method to uncover the underlying regulatory associations between genes and phenotypes. Then, stochastic perturbation strategies are used to create two views for both original and diffusive graphs. Lastly, a multiview contrastive learning paradigm loss is designed to unify the node presentations learned from the two views to resist interference and reduce noise. We perform experiments to validate the performance of CSGDN in three crop datasets: Gossypium hirsutum, Brassica napus, and Triticum turgidum. The results show that the proposed model outperforms state-of-the-art methods by up to 9. 28% AUC for the prediction of link sign in the G. hirsutum dataset. The source code of our model is available at https://github.com/Erican-Ji/CSGDN.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

CircRNAs: A Promising Star for Treatment and Prognosis in Oral Squamous Cell Carcinoma

CircRNAs are a class of endogenous long non-coding RNAs with a single-stranded circular structure. Most circRNAs are relatively stable, highly conserved, and specifically expressed in tissue during the cell and developmental stages. Many circRNAs have been discovered in OSCC. OSCC is one of the most severe and frequent forms of head and neck cancer today, with a poor prognosis and low overall survival rate. Due to its prevalence, OSCC is a global health concern, characterized by genetic and epigenomic changes. However, the mechanism remains vague. With the advancement of biotechnology, a large number of circRNAs have been discovered in mammalian cells. CircRNAs are dysregulated in OSCC tissues and thus associated with the clinicopathological characteristics and prognosis of OSCC patients. Research studies have demonstrated that circRNAs can serve as biomarkers for OSCC diagnosis and treatment. Here, we summarized the properties, functions, and biogenesis of circRNAs, focusing on the progress of current research on circRNAs in OSCC.

Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease

Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.

Benchmarking of computational methods for predicting circRNA-disease associations

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.