MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

Expanding roles of circRNAs in cardiovascular diseases

CircRNAs are a class of single-stranded RNAs characterized by covalently looped structures. Emerging advances have promoted our understanding of circRNA biogenesis, nuclear export, biological functions, and functional mechanisms. Roles of circRNAs in diverse diseases have been increasingly recognized in the past decade, with novel approaches in bioinformatics analysis and new strategies in modulating circRNA levels, which have made circRNAs the hot spot for therapeutic applications. Moreover, due to the intrinsic features of circRNAs such as high stability, conservation, and tissue-/stage-specific expression, circRNAs are believed to be promising prognostic and diagnostic markers for diseases. Aiming cardiovascular disease (CVD), one of the leading causes of mortality worldwide, we briefly summarize the current understanding of circRNAs, provide the recent progress in circRNA functions and functional mechanisms in CVD, and discuss the future perspectives both in circRNA research and therapeutics based on existing knowledge.

Analysis of MicroRNA Cargo in Circulating Extracellular Vesicles from HIV-Infected Individuals with Pulmonary Hypertension

The risk of developing pulmonary hypertension (PH) in people living with HIV is at least 300-fold higher than in the general population, and illicit drug use further potentiates the development of HIV-associated PH. The relevance of extracellular vesicles (EVs) containing both coding as well as non-coding RNAs in PH secondary to HIV infection and drug abuse is yet to be explored. We here compared the miRNA cargo of plasma-derived EVs from HIV-infected stimulant users with (HIV + Stimulants + PH) and without PH (HIV + Stimulants) using small RNA sequencing. The data were compared with 12 PH datasets available in the GEO database to identify potential candidate gene targets for differentially altered miRNAs using the following functional analysis tools: ingenuity pathway analysis (IPA), over-representation analysis (ORA), and gene set enrichment analysis (GSEA). MiRNAs involved in promoting cell proliferation and inhibition of intrinsic apoptotic signaling pathways were among the top upregulated miRNAs identified in EVs from the HIV + Stimulants + PH group compared to the HIV + Stimulants group. Alternatively, the downregulated miRNAs in the HIV + Stimulants + PH group suggested an association with the negative regulation of smooth muscle cell proliferation, IL-2 mediated signaling, and transmembrane receptor protein tyrosine kinase signaling pathways. The validation of significantly differentially expressed miRNAs in an independent set of HIV-infected (cocaine users and nondrug users) with and without PH confirmed the upregulation of miR-32-5p, 92-b-3p, and 301a-3p positively regulating cellular proliferation and downregulation of miR-5571, -4670 negatively regulating smooth muscle proliferation in EVs from HIV-PH patients. This increase in miR-301a-3p and decrease in miR-4670 were negatively correlated with the CD4 count and FEV1/FVC ratio, and positively correlated with viral load. Collectively, this data suggest the association of alterations in the miRNA cargo of circulating EVs with HIV-PH.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity

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.

NFMCLDA: Predicting miRNA-based lncRNA-disease associations by network fusion and matrix completion

In recent years, emerging evidence has revealed a strong association between dysregulations of long non-coding RNAs (lncRNAs) and sophisticated human diseases. Biological experiments are adequate to identify such associations, but they are costly and time-consuming. Therefore, developing high-quality computational methods is a challenging and urgent task in the field of bioinformatics. This paper proposes a new lncRNA-disease association inference approach NFMCLDA (Network Fusion and Matrix Completion lncRNA-Disease Association), which can effectively integrate multi-source association data. In this approach, miRNA information is used as the transition path, and an unbalanced random walk method on three-layer heterogeneous network is adopted in the preprocessing. Therefore, more effective information between networks can be mined and the sparsity problem of the association matrix can be solved. Finally, the matrix completion method accurately predicts associations. The results show that NFMCLDA can provide more accurate lncRNA-disease associations than state-of-the-art methods. The areas under the receiver operating characteristic curves are 0.9648 and 0.9713, respectively, through the cross-validation of 5-fold and 10-fold. Data from published case studies on four diseases - lung cancer, osteosarcoma, cervical cancer, and colon cancer - have confirmed the reliable predictive potential of NFMCLDA model.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

ncRS: A resource of non-coding RNAs in sepsis

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

BACKGROUND

Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods.

RESULTS

Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations.

CONCLUSIONS

Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

BACKGROUND

Sex differences pose a challenge and an opportunity in biomedical research. Understanding how sex chromosomes and hormones affect disease-causing mechanisms will shed light on the mechanisms underlying predominantly idiopathic sex-biased neurodevelopmental disorders such as ADHD, schizophrenia, and autism. Gene expression is a crucial conduit for the influence of sex on developmental processes; therefore, this study focused on sex differences in gene expression and the regulation of gene expression. The increasing interest in microRNAs (miRNAs), small, non-coding RNAs, for their contribution to normal and pathological neurodevelopment prompted us to test how miRNA expression differs between the sexes in the developing brain.

METHODS

High-throughput sequencing approaches were used to identify transcripts, including miRNAs, that showed significantly different expression between male and female brains on day 15.5 of development (E15.5).

RESULTS

Robust sex differences were identified for some genes and miRNAs, confirming the influence of biological sex on RNA. Many miRNAs that exhibit the greatest differences between males and females have established roles in neurodevelopment, implying that sex-biased expression may drive sex differences in developmental processes. In addition to highlighting sex differences for individual miRNAs, gene ontology analysis suggested several broad categories in which sex-biased RNAs might act to establish sex differences in the embryonic mouse brain. Finally, mining publicly available SNP data indicated that some sex-biased miRNAs reside near the genomic regions associated with neurodevelopmental disorders.

CONCLUSIONS

Together, these findings reinforce the importance of cataloguing sex differences in molecular biology research and highlight genes, miRNAs, and pathways of interest that may be important for sexual differentiation in the mouse and possibly the human brain.

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

iSnoDi-MDRF: Identifying snoRNA-Disease Associations Based on Multiple Biological Data by Ranking Framework

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/.

Circular stable intronic RNAs possess distinct biological features and are deregulated in bladder cancer

Until recently, intronic lariats were regarded as short-lasting splicing byproducts with no apparent function; however, increasing evidence of stable derivatives suggests regulatory roles. Yet little is known about their characteristics, functions, distribution, and expression in healthy and tumor tissue. Here, we profiled and characterized circular stable intronic sequence RNAs (sisRNAs) using total RNA-Seq data from bladder cancer (BC; n = 457, UROMOL cohort), healthy tissue (n = 46), and fractionated cell lines (n = 5). We found that the recently-discovered full-length intronic circles and the stable lariats formed distinct subclasses, with a surprisingly high intronic circle fraction in BC (∼45%) compared to healthy tissues (0-20%). The stable lariats and their host introns were characterized by small transcript sizes, highly conserved BP regions, enriched BP motifs, and localization in multiple cell fractions. Additionally, circular sisRNAs showed tissue-specific expression patterns. We found nine circular sisRNAs as differentially expressed across early-stage BC patients with different prognoses, and sisHNRNPK expression correlated with progression-free survival. In conclusion, we identify distinguishing biological features of circular sisRNAs and point to specific candidates (incl. sisHNRNPK, sisWDR13 and sisMBNL1) that were highly expressed, had evolutionary conserved sequences, or had clinical correlations, which may facilitate future studies and further insights into their functional roles.

Long Non-Coding RNAs in Colorectal Cancer: Navigating the Intersections of Immunity, Intercellular Communication, and Therapeutic Potential

This comprehensive review elucidates the intricate roles of long non-coding RNAs (lncRNAs) within the colorectal cancer (CRC) microenvironment, intersecting the domains of immunity, intercellular communication, and therapeutic potential. lncRNAs, which are significantly involved in the pathogenesis of CRC, immune evasion, and the treatment response to CRC, have crucial implications in inflammation and serve as promising candidates for novel therapeutic strategies and biomarkers. This review scrutinizes the interaction of lncRNAs with the Consensus Molecular Subtypes (CMSs) of CRC, their complex interplay with the tumor stroma affecting immunity and inflammation, and their conveyance via extracellular vesicles, particularly exosomes. Furthermore, we delve into the intricate relationship between lncRNAs and other non-coding RNAs, including microRNAs and circular RNAs, in mediating cell-to-cell communication within the CRC microenvironment. Lastly, we propose potential strategies to manipulate lncRNAs to enhance anti-tumor immunity, thereby underlining the significance of lncRNAs in devising innovative therapeutic interventions in CRC.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Comparative microRNA profiling of Trypanosoma cruzi infected human cells

Introduction

Trypanosoma cruzi, the causative agent of Chagas disease, can infect almost any nucleated cell in the mammalian host. Although previous studies have described the transcriptomic changes that occur in host cells during parasite infection, the understanding of the role of post-transcriptional regulation in this process is limited. MicroRNAs, a class of short non-coding RNAs, are key players in regulating gene expression at the post-transcriptional level, and their involvement in the host-T. cruzi interplay is a growing area of research. However, to our knowledge, there are no comparative studies on the microRNA changes that occur in different cell types in response to T. cruzi infection.

Methods and results

Here we investigated microRNA changes in epithelial cells, cardiomyocytes and macrophages infected with T. cruzi for 24 hours, using small RNA sequencing followed by careful bioinformatics analysis. We show that, although microRNAs are highly cell type-specific, a signature of three microRNAs -miR-146a, miR-708 and miR-1246, emerges as consistently responsive to T. cruzi infection across representative human cell types. T. cruzi lacks canonical microRNA-induced silencing mechanisms and we confirm that it does not produce any small RNA that mimics known host microRNAs. We found that macrophages show a broad response to parasite infection, while microRNA changes in epithelial and cardiomyocytes are modest. Complementary data indicated that cardiomyocyte response may be greater at early time points of infection.

Conclusions

Our findings emphasize the significance of considering microRNA changes at the cellular level and complement previous studies conducted at higher organizational levels, such as heart samples. While miR-146a has been previously implicated in T. cruzi infection, similarly to its involvement in many other immunological responses, miR-1246 and miR-708 are demonstrated here for the first time. Given their expression in multiple cell types, we anticipate our work as a starting point for future investigations into their role in the post-transcriptional regulation of T. cruzi infected cells and their potential as biomarkers for Chagas disease.

iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network

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.

Multi-omics study and ncRNA regulation of anti-BmNPV in silkworms, Bombyx mori: an update

Bombyx mori silkworm is an important economic insect which has a significant contribution to the improvement of the economy. Bombyx mori nucleopolyhedrovirus (BmNPV) is a vitally significant purulent virus that impedes the sustainable and stable development of the silkworm industry, resulting in substantial economic losses. In recent years, with the development of biotechnology, transcriptomics, proteomics, metabolomics, and the related techniques have been used to select BmNPV-resistant genes, proteins, and metabolites. The regulatory networks between viruses and hosts have been gradually clarified with the discovery of ncRNAs, such as miRNA, lncRNA, and circRNA in cells. Thus, this paper aims to highlight the results of current multi-omics and ncRNA studies on BmNPV resistance in the silkworm, providing some references for resistant strategies in the silkworm to BmNPV.

miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations

MicroRNAs (miRNAs) are small non-coding RNAs that play a critical role in regulating diverse biological processes. Extracting functional insights from a list of miRNAs is challenging, as each miRNA can potentially interact with hundreds of genes. To address this challenge, we developed miEAA, a flexible and comprehensive miRNA enrichment analysis tool based on direct and indirect miRNA annotation. The latest release of miEAA includes a data warehouse of 19 miRNA repositories, covering 10 different organisms and 139 399 functional categories. We have added information on the cellular context of miRNAs, isomiRs, and high-confidence miRNAs to improve the accuracy of the results. We have also improved the representation of aggregated results, including interactive Upset plots to aid users in understanding the interaction among enriched terms or categories. Finally, we demonstrate the functionality of miEAA in the context of ageing and highlight the importance of carefully considering the miRNA input list. MiEAA is free to use and publicly available at https://www.ccb.uni-saarland.de/mieaa/.

Predicting disease genes based on multi-head attention fusion

BACKGROUND

The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes.

RESULTS

This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association.

CONCLUSIONS

The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Data resources and computational methods for lncRNA-disease association prediction

Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.

Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation

BACKGROUND

Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far.

RESULTS

Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease subgraphs for the first time, followed by a residual scaling-based feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDA-PRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dual-loss mechanism into GCN for PDA prediction. Compared with state-of-the-art approaches, PDA-PRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDA-PRGCN can reliably detect PDAs.

CONCLUSION

PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

A Cocktail of Polyherbal Bioactive Compounds and Regular Mobility Training as Senolytic Approaches in Age-dependent Alzheimer's: the In Silico Analysis, Lifestyle Intervention in Old Age

Alzheimer's is a principal concern globally. Machine learning is a valuable tool to determine protective and diagnostic approaches for the elderly. We analyzed microarray datasets of Alzheimer's cases based on artificial intelligence by R statistical software. This study provided a screened pool of ncRNAs and coding RNAs related to Alzheimer's development. We designed hub genes as cut points in networks and predicted potential microRNAs and LncRNA to regulate protein networks in aging and Alzheimer's through in silico algorithms. Notably, we collected effective traditional herbal medicines. A list of bioactive compounds prepared including capsaicin, piperine, crocetin, safranal, saffron oil, coumarin, thujone, rosmarinic acid, sabinene, thymoquinone, ascorbic acid, vitamin E, cyanidin, rhaponticin, isovitexin, coumarin, nobiletin, evodiamine, gingerol, curcumin, quercetin, fisetin, and allicin as an effective fusion that potentially modulates hub proteins and molecular signaling pathways based on pharmacophore model screening and chemoinformatics survey. We identified profiles of 21 mRNAs, 272 microRNAs, and eight LncRNA in Alzheimer's based on prediction algorithms. We suggested a fusion of senolytic herbal ligands as an alternative therapy and preventive formulation in dementia. Also, we provided ncRNAs expression status as novel monitoring strategies in Alzheimer's and new cut-point proteins as novel therapeutic approaches. Synchronizing fusion drugs and lifestyle could reverse Alzheimer's hallmarks to amelioration via an offset of the signaling pathways, leading to increased life quality in the elderly.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints

Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.

Deep learning models for disease-associated circRNA prediction: a review

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

lncRNASNP v3: an updated database for functional variants in long non-coding RNAs

Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.

LncTarD 2.0: an updated comprehensive database for experimentally-supported functional lncRNA-target regulations in human diseases

An updated LncTarD 2.0 database provides a comprehensive resource on key lncRNA-target regulations, their influenced functions and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD 2.0 is freely available at (http://bio-bigdata.hrbmu.edu.cn/LncTarD or https://lnctard.bio-database.com/). LncTarD 2.0 was updated with several new features, including (i) an increased number of disease-associated lncRNA entries, where the current release provides 8360 key lncRNA-target regulations, with 419 disease subtypes and 1355 lncRNAs; (ii) predicted 3312 out of 8360 lncRNA-target regulations as potential diagnostic or therapeutic biomarkers in circulating tumor cells (CTCs); (iii) addition of 536 new, experimentally supported lncRNA-target regulations that modulate properties of cancer stem cells; (iv) addition of an experimentally supported clinical application section of 2894 lncRNA-target regulations for potential clinical application. Importantly, LncTarD 2.0 provides RNA-seq/microarray and single-cell web tools for customizable analysis and visualization of lncRNA-target regulations in diseases. RNA-seq/microarray web tool was used to mining lncRNA-target regulations in both disease tissue samples and CTCs blood samples. The single-cell web tools provide single-cell lncRNA-target annotation from the perspectives of pan-cancer analysis and cancer-specific analysis at the single-cell level. LncTarD 2.0 will be a useful resource and mining tool for the investigation of the functions and mechanisms of lncRNA deregulation in human disease.

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications

RNA modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Abnormal changes of WERs will disrupt the RNA modification homeostasis of their target genes, leading to the dysregulation of RNA metabolisms such as RNA stability and translation, and consequently to diseases such as cancer. A public repository hosting the regulatory relationships between WERs and their target genes will help in understanding the roles of RNA modifications in various physiological and pathological conditions. Previously, we developed a database named 'm6A2Target' to host targets of WERs in m6A, one of the most prevalent RNA modifications in eukaryotic cells. To host all RNA modification (RM)-related WER-target associations, we hereby present an updated database, named 'RM2Target' (http://rm2target.canceromics.org/). In this update, RM2Target encompasses 1 619 653 WER-target associations for nine RNA modifications in human and mouse, including m6A, m6Am, m5C, m5U, m1A, m7G, pseudouridine, 2'-O-Me and A-to-I. Extensive annotations of target genes are available in RM2Target, including but not limited to basic gene information, RNA modifications, RNA-RNA/RNA-protein interactions and related diseases. Altogether, we expect that RM2Target will facilitate further downstream functional and mechanistic studies in the field of RNA modification research.

iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network

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/.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction

Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.

Long noncoding RNA H19 alleviates inflammation in osteoarthritis through interactions between TP53, IL-38, and IL-36 receptor

Aims

Osteoarthritis (OA) is a common degenerative joint disease characterized by chronic inflammatory articular cartilage degradation. Long noncoding RNAs (lncRNAs) have been previously indicated to play an important role in inflammation-related diseases. Herein, the current study set out to explore the involvement of lncRNA H19 in OA.

Methods

Firstly, OA mouse models and interleukin (IL)-1β-induced mouse chondrocytes were established. Expression patterns of IL-38 were determined in the synovial fluid and cartilage tissues from OA patients. Furthermore, the targeting relationship between lncRNA H19, tumour protein p53 (TP53), and IL-38 was determined by means of dual-luciferase reporter gene, chromatin immunoprecipitation, and RNA immunoprecipitation assays. Subsequent to gain- and loss-of-function assays, the levels of cartilage damage and proinflammatory factors were further detected using safranin O-fast green staining and enzyme-linked immunosorbent assay (ELISA) in vivo, respectively, while chondrocyte apoptosis was measured using Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) in vitro.

Results

IL-38 was highly expressed in lentivirus vector-mediated OA mice. Meanwhile, injection of exogenous IL-38 to OA mice alleviated the cartilage damage, and reduced the levels of proinflammatory factors and chondrocyte apoptosis. TP53 was responsible for lncRNA H19-mediated upregulation of IL-38. Furthermore, it was found that the anti-inflammatory effects of IL-38 were achieved by its binding with the IL-36 receptor (IL-36R). Overexpression of H19 reduced the expression of inflammatory factors and chondrocyte apoptosis, which was abrogated by knockdown of IL-38 or TP53.

Conclusion

Collectively, our findings evidenced that upregulation of lncRNA H19 attenuates inflammation and ameliorates cartilage damage and chondrocyte apoptosis in OA by upregulating TP53, IL-38, and by activating IL-36R.

Cite this article: Bone Joint Res 2022;11(8):594–607.

Prediction of biomarker-disease associations based on graph attention network and text representation

MOTIVATION

The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers.

RESULTS

Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods.

AVAILABILITY

The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.

PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization

Many studies have proved that small nucleolar RNAs (snoRNAs) play critical roles in the development of various human complex diseases. Discovering the associations between snoRNAs and diseases is an important step toward understanding the pathogenesis and characteristics of diseases. However, uncovering associations via traditional experimental approaches is costly and time-consuming. This study proposed a bounded nuclear norm regularization-based method, called PSnoD, to predict snoRNA-disease associations. Benchmark experiments showed that compared with the state-of-the-art methods, PSnoD achieved a superior performance in the 5-fold stratified shuffle split. PSnoD produced a robust performance with an area under receiver-operating characteristic of 0.90 and an area under precision-recall of 0.55, highlighting the effectiveness of our proposed method. In addition, the computational efficiency of PSnoD was also demonstrated by comparison with other matrix completion techniques. More importantly, the case study further elucidated the ability of PSnoD to screen potential snoRNA-disease associations. The code of PSnoD has been uploaded to https://github.com/linDing-groups/PSnoD. Based on PSnoD, we established a web server that is freely accessed via http://psnod.lin-group.cn/.