MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association

MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest

Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of traditional experiments in verifying the associations of miRNAs and diseases (MDAs), a variety of computational methods have emerged. However, these methods still have many shortcomings in terms of predictive performance and accuracy. In this study, a model based on multiple graph convolutional networks and random forest (MGCNRF) was proposed for the prediction MDAs. Specifically, MGCNRF first mapped miRNA functional similarity and sequence similarity, disease semantic similarity and target similarity, and the known MDAs into four different two-layer heterogeneous networks. Second, MGCNRF applied four heterogeneous networks into four different layered attention graph convolutional networks (GCNs), respectively, to extract MDA embeddings. Finally, MGCNRF integrated the embeddings of every MDA into the features of the miRNA-disease pair and predicted potential MDAs through the random forest (RF). Fivefold cross-validation was applied to verify the prediction performance of MGCNRF, which outperforms the other seven state-of-the-art methods by area under curve. Furthermore, the accuracy and the case studies of different diseases further demonstrate the scientific rationale of MGCNRF. In conclusion, MGCNRF can serve as a scientific tool for predicting potential MDAs.

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives.

Deep neural network model for enhancing disease prediction using auto encoder based broad learning

Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction

MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGANMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGANMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNA-disease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGANMDA in predicting unknown diseases.

Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features

MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.

Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks

Numerous experiments have shown that the occurrence of complex human diseases is often accompanied by abnormal expression of microRNA (miRNA). Identifying the associations between miRNAs and diseases is of great significance in the development of clinical medicine. However, traditional experimental methods are often time-consuming and inefficient. To this end, we proposed a deep learning method based on neighbor selection graph attention networks for predicting miRNA-disease associations (NSAMDA). Specifically, we firstly fused miRNA sequence similarity information and miRNA integrated similarity information to enrich miRNA feature information. Secondly, we used the fused miRNA feature information and disease integrated similarity information to construct a miRNA-disease heterogeneous graph. Thirdly, we introduced a neighbor selection method based on graph attention networks to select k-most important neighbors for aggregation. Finally, we used the inner product decoder to score miRNA-disease pairs. The results of five-fold cross-validation show that the mean AUC of NSAMDA is 93.69% on HMDD v2.0 dataset. In addition, case studies on the esophageal neoplasm, lung neoplasm and lymphoma were carried out to further confirm the effectiveness of the NSAMDA model. The results showed that the NSAMDA method achieves satisfactory performance on predicting miRNA-disease associations and is superior to the most advanced model.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting

An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA–disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.

RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA-disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association

It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.