IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

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.

HGNNLDA: Predicting lncRNA-Drug Sensitivity Associations via a Dual Channel Hypergraph Neural Network.

Drug sensitivity is critical for enabling personalized treatment. Many studies have shown that long non-coding RNAs (lncRNAs) are closely related to drug sensitivity because lncRNAs can regulate genes related to drug sensitivity to affect drug efficacy. Exploring lncRNA-drug sensitivity associations has important implications for drug development and disease treatment. However, identifying lncRNA-drug sensitivity associations based on traditional biological approaches is small-scale and time-consuming. In this work, we develop a dual-channel hypergraph neural network-based method named HGNNLDA to infer unknown lncRNA-drug sensitivity associations. To our best knowledge, HGNNLDA is the first computational framework to predict lncRNA-drug sensitivity associations. HGNNLDA applies the hypergraph neural network to obtain high-order neighbor information on the lncRNA hypergraph and the drug hypergraph, respectively, and utilizes a joint update mechanism to generate lncRNA embeddings and drug embeddings. In traditional graphs, an edge contains only two nodes. However, hyperedges in hypergraphs can contain any number of nodes and hypergraphs can well describe the higher-order connectivity of the lncRNA-drug bipartite graphs. The comprehensive experimental results show that HGNNLDA significantly outperforms the other six state-of-the-art models. Case studies on two drugs further illustrate that HGNNLDA is an effective tool to predict lncRNA-drug sensitivity associations.

MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.

BACKGROUND: Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. RESULTS: In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. CONCLUSIONS: Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.

MGATMDA: Predicting Microbe-Disease Associations via Multi-Component Graph Attention Network.

Microbes are parasitic in various human body organs and play significant roles in a wide range of diseases. Identifying microbe-disease associations is conducive to the identification of potential drug targets. Considering the high cost and risk of biological experiments, developing computational approaches to explore the relationship between microbes and diseases is an alternative choice. However, most existing methods are based on unreliable or noisy similarity, and the prediction accuracy could be affected. Besides, it is still a great challenge for most previous methods to make predictions for the large-scale dataset. In this work, we develop a multi-component Graph Attention Network (GAT) based framework, termed MGATMDA, for predicting microbe-disease associations. MGATMDA is built on a bipartite graph of microbes and diseases. It contains three essential parts: decomposer, combiner, and predictor. The decomposer first decomposes the edges in the bipartite graph to identify the latent components by node-level attention mechanism. The combiner then recombines these latent components automatically to obtain unified embedding for prediction by component-level attention mechanism. Finally, a fully connected network is used to predict unknown microbes-disease associations. Experimental results showed that our proposed method outperformed eight state-of-the-art methods. Case studies for two common diseases further demonstrated the effectiveness of MGATMDA in predicting potential microbe-disease associations. The codes are available at Github https://github.com/dayunliu/MGATMDA.

PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction.

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA-disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA-disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA-disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.

BACKGROUND: Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. RESULTS: In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. CONCLUSION: The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

Relationship between onset of peptic ulcer and meteorological factors.

AIM: To discuss the relationship between onset of peptic ulcer (PU) and meteorological factors (MFs). METHODS: A total of 24,252 patients were found with active PU in 104,121 samples of gastroscopic examination from 17 hospitals in Nanning from 1992 to 1997. The detectable rate of PU (DRPU) was calculated every month, every ten days and every five days. An analysis of DRPU and MFs was made in the same period of the year. A forecast model based on MFs of the previous month was established. The real and forecast values were tested and verified. RESULTS: During the 6 years, the DRPU from November to April was 24.4 -28.8%. The peak value (28.8%) was in January. The DRPU from May to October was 20.0-22.6%, with its low peak (20.0%) in June. The DRPU decreased from winter and spring to summer and autumn (P<0.005). The correlated coefficient between DRPU and average temperature value was -0.8704, -0.6624, -0.5384 for one month, ten days , five days respectively (P<0.01). The correlated coefficient between DRPU and average highest temperature value was -0.8000,-0.6470,-0.5167 respectively (P<0.01). The correlated coefficient between DRPU and average lowest temperature value was -0.8091, -0.6617, -0.5384 respectively (P<0.01). The correlated coefficient between DRPU and average dew point temperature was -0.7812, -0.6246, -0.4936 respectively (P<0.01). The correlated coefficient between DRPU and average air pressure value was 0.7320, 0.5777, 0.4579 respectively (P<0.01). The average temperature, average highest and lowest temperature, average air pressure and average dew point temperature value of the previous month, ten days and five days could forecast the onset of PU, with its real and forecast values corresponding to 71.8%, 67.9% and 66.6% respectively. CONCLUSION: DRPU is closely related with the average temperature, average highest and lowest temperature,average air pressure and average dew point temperature of each month, every ten days and every five days for the same period. When MFs are changed, the human body produces a series of stress actions.A long-term and median-term based medical meteorological forecast of the onset of PU can be made more accurately according to this.

Study of the relationship between the onset of peptic ulcers and meteorological factors.

OBJECTIVE: To discuss the relationship between the onset of peptic ulcers (PU) and meteorological factors (MF). METHODS: In reviewing records from 17 hospitals in the city of Nanning from 1992 to 1997, we found 24, 252 cases of PU in 104, 121 samples of gastroscopic examinations. We then calculated the detectable rate of PU (DRPU) during each season every five days (FD) and made a correlated analysis with the seasonal MF during the same period in Nanning. Finally, we made a multiple regressive correlated analysis of DRPU and the 5MF for the same period of the year. A forecast model based on the MF of the previous FD was established. The real value and the forecast value was being tested and verified. RESULTS: From 1992 to 1997, the DRPU is: winter and spring > summer and autumn (P < 0.005). There is a close relationship between the DRPU and the average temperature (AT), the average highest temperature (AHT), the average lowest temperature (ALT), average air pressure (AAP) and the average dew point temperature (ADT) of the five days of the same period of the year (the correlated coefficients are -0.5348, -05167, -0.5384, 0.4579 and -0.4936, respectively), with P < 0.01. The AT, AHT, ALT, AAP and ADT of the previous FD are of great value in forecasting the onset of PU, with its real value and forecast value corresponding to 66.6%. CONCLUSIONS: There exists a close relationship between DRPU and the AT, AHT, ALT, AAP and ADT of the FD for the same period. A mid-term medical meteorological forecast of the onset of PU can be made more accurately and reliably according to the close relation between the DRPU and some MF of the previous FD.

Clinical epidemiological study on intrahepatic cholelithiasis: analysis of 8585 cases.

OBJECTIVE: To investigate the clinical epidemiology of intrahepatic cholelithiasis in Guangxi area, China. METHODS: 8585 cases of cholelithiasis proved by surgery in a period of 19 years were analyzed retrospectively. Data were collected and analyzed by computer software package PEMS. RESULTS: Cases of intrahepatic cholelithiasis accounted for more than one third of cases of cholelithiasis treated in the same period. The prevalence of intrahepatic cholelithiasis in farmers increased from 23.4% out of all cases with gallstone in 1981-1985 to 55.8% in 1991-1999. The constituent ratio of intrahepatic cholelithiasis in males was nearly the same in females. The peak prevalence age of patients with intrahepatic cholelithiasis ranged from 31 to 40 years, and the mortality was the highest among all bile stone cases. CONCLUSION: Intrahepatic cholelithiasis is by no means a vanishing disease, especially in rural area.