M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction.

Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases is critical. In this paper, a new microbe-disease association prediction model is proposed that combines a multi-view multi-modal network and a multi-scale feature fusion mechanism, called M3HOGAT. Firstly, a microbe-disease association network and multiple similarity views are constructed based on multi-source information. Then, consider that neighbor information from disparate orders might be more adept at learning node representations. Consequently, the higher-order graph attention network (HOGAT) is devised to aggregate neighbor information from disparate orders to extract microbe and disease features from different networks and views. Given that the embedding features of microbe and disease from different views possess varying importance, a multi-scale feature fusion mechanism is employed to learn their interaction information, thereby generating the final feature of microbes and diseases. Finally, an inner product decoder is used to reconstruct the microbe-disease association matrix. Compared with five state-of-the-art methods on the HMDAD and Disbiome datasets, the results of 5-fold cross-validations show that M3HOGAT achieves the best performance. Furthermore, case studies on asthma and obesity confirm the effectiveness of M3HOGAT in identifying potential disease-related microbes.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.

Acellular dermal matrix in urethral reconstruction.

The management of severe urethral stricture has always posed a formidable challenge. Traditional approaches such as skin flaps, mucosal grafts, and urethroplasty may not be suitable for lengthy and intricate strictures. In the past two decades, tissue engineering solutions utilizing acellular dermal matrix have emerged as potential alternatives. Acellular dermal matrix (ADM) is a non-immunogenic biological collagen scaffold that has demonstrated its ability to induce layer-by-layer tissue regeneration. The application of ADM in urethral reconstruction through tissue engineering has become a practical endeavor. This article provides an overview of the preparation, characteristics, advantages, and disadvantages of ADM along with its utilization in urethral reconstruction via tissue engineering.

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

Two-year outcomes of anti-reflux mucosectomy in treating gastroesophageal reflux disease: A Chinese prospective cohort study.

OBJECTIVES: Anti-reflux mucosectomy (ARMS) is an emerging and promising endoscopic treatment for gastroesophageal reflux disease (GERD). In the current study we aimed to evaluate the safety and efficacy of ARMS in treating Chinese GERD patients. METHODS: This was a single-center prospective cohort study. ARMS was performed in GERD patients by an experienced endoscopist. The patients were required to undergo symptom assessment as well as endoscopic examination, high-resolution manometry (HRM), and impedance-pH monitoring before and after ARMS. RESULTS: Twelve patients were enrolled. Follow-up was completed by all patients at 3 and 6 months, 11 patients at 1 year, and 8 patients at 2 years after ARMS, respectively. Symptom improvement was achieved in 66.7%, 75.0%, 72.7%, and 50.0% of the patients at 3 months, 6 months, 1 year, and 2 years after ARMS, respectively. Postoperative dysphagia was reported by 25.0%, 25.0%, 27.3%, and 25.0% of patients at 3 months, 6 months, 1 year, and 2 years after surgery, none of whom required additional invasive treatment. All patients with preoperative esophagitis healed after ARMS. For impedance-pH monitoring parameters, number of acidic reflux episodes and the proportion of patients with acid exposure time (AET) >4.0% decreased significantly after ARMS. CONCLUSIONS: ARMS was safe and effective in Chinese GERD patients. The efficacy of ARMS was not short-term and remained evident throughout the 2-year follow-up. Further multicenter studies with larger sample sizes are needed to verify our findings.

Patients at risk for further examination with conventional gastroscopy after undergoing magnetically controlled capsule endoscopy.

OBJECTIVE: In this study we aimed to compare the need for further examination with conventional gastroscopy within 1 year after magnetically assisted capsule endoscopy (MCCE) examination between patients with gastrointestinal (GI) symptoms and asymptomatic individuals. METHODS: After propensity score matching analysis, 372 patients with GI symptoms and 372 asymptomatic individuals who had undergone MCCE at the First Affiliated Hospital of Sun Yat-sen University from January 1, 2019 to December 30, 2020 were retrospectively enrolled. Demographic and clinical characteristics of the participants and their MCCE and gastroscopic findings (performed within 1 year after MCCE) were analyzed. RESULTS: Fifty-one (6.85%) patients underwent further examination with conventional gastroscopy within 1 year after MCCE. Those with GI symptoms were more likely to undergo conventional gastroscopy than those without (9.95% vs 3.76%, P < 0.001). Polyps were the most common finding of MCCE. The rate of conventional gastroscopy in patients with focal lesions was significantly higher than that in those without focal lesions (P < 0.05). However, such rate did not differ in the different age groups (P = 0.106). CONCLUSIONS: MCCE is an optimal alternative for gastric examination, especially for large-scale screening of asymptomatic individuals. Patients with GI symptoms or focal lesions detected by MCCE are more likely to seek further examination with conventional gastroscopy for biopsy or endoscopic treatment than those without.

Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data.

Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which constitutes a critical step within the data processing workflow. However, existing methods for cell type identification through learning low-dimensional latent embeddings often overlook the intercellular structural relationships. In this paper, we present a novel non-negative low-rank similarity correction model (NLRSIM) that leverages subspace clustering to preserve the global structure among cells. This model introduces a novel manifold learning process to address the issue of imbalanced neighbourhood spatial density in cells, thereby effectively preserving local geometric structures. This procedure utilizes a position-sensitive hashing algorithm to construct the graph structure of the data. The experimental results demonstrate that the NLRSIM surpasses other advanced models in terms of clustering effects and visualization experiments. The validated effectiveness of gene expression information after calibration by the NLRSIM model has been duly ascertained in the realm of relevant biological studies. The NLRSIM model offers unprecedented insights into gene expression, states, and structures at the individual cellular level, thereby contributing novel perspectives to the field.

Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning.

Clinical trials indicate that the dysregulation of microRNAs (miRNAs) is closely associated with the development of diseases. Therefore, predicting miRNA-disease associations is significant for studying the pathogenesis of diseases. Since traditional wet-lab methods are resource-intensive, cost-saving computational models can be an effective complementary tool in biological experiments. In this work, a locality-constrained linear coding is proposed to predict associations (ILLCEL). Among them, ILLCEL adopts miRNA sequence similarity, miRNA functional similarity, disease semantic similarity, and interaction profile similarity obtained by locality-constrained linear coding (LLC) as the priori information. Next, features and similarities extracted from multiperspectives are input to the ensemble learning framework to improve the comprehensiveness of the prediction. Significantly, the introduction of hypergraph-regular terms improves the accuracy of prediction by describing complex associations between samples. The results under fivefold cross validation indicate that ILLCEL achieves superior prediction performance. In case studies, known associations are accurately predicted and novel associations are verified in HMDD v3.2, miRCancer, and existing literature. It is concluded that ILLCEL can be served as a powerful tool for inferring potential associations.

A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations.

Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

Clinical guideline on magnetically controlled capsule gastroscopy (2021 edition).

With the development and generalization of endoscopic technology and screening, clinical application of magnetically controlled capsule gastroscopy (MCCG) has been increasing. In recent years, various types of MCCG are used globally. Therefore, establishing relevant guidelines on MCCG is of great significance. The current guidelines containing 23 statements were established based on clinical evidence and expert opinions, mainly focus on aspects including definition and diagnostic accuracy, application population, technical optimization, inspection process, and quality control of MCCG. The level of evidence and strength of recommendations were evaluated. The guidelines are expected to guide the standardized application and scientific innovation of MCCG for the reference of clinicians.

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder.

Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.

Efficacy and Safety of Keverprazan Compared With Lansoprazole in the Treatment of Duodenal Ulcer: A Phase III, Randomized, Double-Blind, Multicenter Trial.

INTRODUCTION: Keverprazan is a novel potassium-competitive acid blocker for the treatment of acid-related disorders requiring potent acid inhibition. This study aimed to establish the noninferiority of keverprazan to lansoprazole in the treatment of patients with duodenal ulcer (DU). METHODS: In this phase III, double-blind, multicenter study, 360 Chinese patients with endoscopically confirmed active DU were randomized 1:1 to take either keverprazan (20 mg) or lansoprazole (30 mg) treatment for up to 6 weeks. The primary end point was DU healing rate at week 6. The secondary end point was DU healing rate at week 4. Symptom improvement and safety were also assessed. RESULTS: Based on the full analysis set, the cumulative healing rates at week 6 were 94.4% (170/180) and 93.3% (166/178) for keverprazan and lansoprazole, respectively (difference: 1.2%; 95% confidence intervel: -4.0%-6.5%). At week 4, the respective healing rates were 83.9% (151/180) and 80.3% (143/178). In the per protocol set, the 6-week healing rates in keverprazan and lansoprazole groups were 98.2% (163/166) and 97.6% (163/167), respectively (difference: 0.6%; 95% confidence intervel: -3.1%-4.4%); the 4-week healing rates were respectively 86.8% (144/166) and 85.6% (143/167). Keverprazan was noninferior to lansoprazole in DU healing after the treatment for 4 and 6 weeks. The incidence of treatment-emergent adverse events was comparable among groups. DISCUSSION: Keverprazan 20 mg had a good safety profile and was noninferior to lansoprazole 30 mg once daily for DU healing.

Exploring the common diagnostic gene KCNJ15 and shared pathway of ankylosing spondylitis and ulcerative colitis through integrated bioinformatics.

Introduction: The similarity between ankylosing spondylitis (AS) and ulcerative colitis (UC) in incidence rate and pathogenesis has been revealed. But the common pathogenesis that explains the relationship between AS and UC is still lacked, and the related genetic research is limited. We purposed to explore shared biomarkers and pathways of AS and UC through integrated bioinformatics. Methods: Gene expression data of AS and UC were obtained in the GEO database. We applied weighted gene co-expression network analysis (WGCNA) to identify AS-related and UC-related co-expression gene modules. Subsequently, machine learning algorithm was used to further screen hub genes. We validated the expression level and diagnostic efficiency of the shared diagnostic gene of AS and UC in external datasets. Gene set enrichment analysis (GSEA) was applied to analyze pathway-level changes between disease group and normal group. Finally, we analyzed the relationship between hub biomarker and immune microenvironment by using the CIBERSORT deconvolution algorithm. Results: 203 genes were obtained by overlapping AS-related gene module and UC-related gene module. Through SVM-RFE algorithm, 19 hub diagnostic genes were selected for AS in GSE25101 and 6 hub diagnostic genes were selected for UC in GSE94648. KCNJ15 was obtained as a common diagnostic gene of AS and UC. The expression of KCNJ15 was validated in independent datasets, and the results showed that KCNJ15 were similarly upregulated in AS samples and UC samples. Besides, ROC analysis also revealed that KCNJ15 had good diagnostic efficacy. The GSEA analysis revealed that oxidative phosphorylation pathway was the shared pathway of AS and UC. In addition, CIBERSORT results revealed the correlation between KCNJ15 gene and immune microenvironment in AS and UC. Conclusion: We have explored a common diagnostic gene KCNJ15 and a shared oxidative phosphorylation pathway of AS and UC through integrated bioinformatics, which may provide a potential diagnostic biomarker and novel insight for studying the mechanism of AS-related UC.

Three new MnII-[MoIII(CN)7]4- molecular magnets constructed from chiral bidentate chelating ligands.

Three new cyanide-bridged compounds {[Mn((S,S)-Dpen)]3[Mn((S,S)-Dpen)(H2O)][Mo(CN)7]2·4H2O·4C2H3N}n (1-SS), {[Mn((R,R)-Dpen)]3[Mn((R,R)-Dpen)(H2O)][Mo(CN)7]2·4.5H2O·4C2H3N}n (1-RR), and {[Mn(Chxn)][Mn(Chxn)(H2O)0.8][Mo(CN)7]·H2O·4C2H3N}n (2) (SS/RR-Dpen = (S,S)/(R,R)-1,2-diphenylethylenediamine and Chxn = 1,2-cyclohexanediamine) have been successfully synthesized from the self-assembly reaction of the [MoIII(CN)7]4- unit, the MnII ions, and two chiral bidentate chelating ligands. Single-crystal structure determinations show that compounds 1-SS and 1-RR containing ligands SS/RR-Dpen are enantiomers and crystallize in the chiral space group P21. On the other hand, compound 2 crystallizes in the achiral centrosymmetric space group P1̄ due to the racemization of the SS/RR-Chxn ligands during the growth of the crystals. Despite their different space groups and ligands, all three compounds exhibit similar framework structures consisting of cyano-bridged MnII-MoIII two-dimensional layers separated by the bidentate ligands. The circular dichroism (CD) spectra have further demonstrated the enantiopure character of compounds 1-SS and 1-RR. Magnetic measurements revealed that all three compounds display ferrimagnetic ordering with similar critical temperatures of about 40 K. The chiral enantiomers 1-SS and 1-RR exhibit the magnetic hysteresis loop with a coercive field of about 8000 Oe at 2 K, which is by far the highest for all known MnII-[MoIII(CN)7]4- magnets. Analyses of their structures and magnetic properties indicated that their magnetic properties depend on the anisotropic magnetic interactions between the MnII and MoIII centers, which are closely related to the C-N-M bond angles.

NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.

Studies have revealed that microbes have an important effect on numerous physiological processes, and further research on the links between diseases and microbes is significant. Given that laboratory methods are expensive and not optimized, computational models are increasingly used for discovering disease-related microbes. Here, a new neighbor approach based on two-tier Bi-Random Walk is proposed for potential disease-related microbes, known as NTBiRW. In this method, the first step is to construct multiple microbe similarities and disease similarities. Then, three kinds of microbe/disease similarity are integrated through two-tier Bi-Random Walk to obtain the final integrated microbe/disease similarity network with different weights. Finally, Weighted K Nearest Known Neighbors (WKNKN) is used for prediction based on the final similarity network. In addition, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV) are applied for evaluating the performance of NTBiRW. Multiple evaluating indicators are taken to show the performance from multiple perspectives. And most of the evaluation index values of NTBiRW are better than those of the compared methods. Moreover, in case studies on atopic dermatitis and psoriasis, most of the first 10 candidates in the final result can be proven. This also demonstrates the capability of NTBiRW for discovering new associations. Therefore, this method can contribute to the discovery of disease-related microbes and thus offer new thoughts for further understanding the pathogenesis of diseases.

A Prognostic Signature for Colon Adenocarcinoma Patients Based on m6A-Related lncRNAs.

N6-methyladenosine (m6A) modification is a common epigenetic modification. It is reported that lncRNA can be regulated by m6A modification. Previous studies have shown that lncRNAs associated with m6A regulation (m6A-lncRNAs) serve as ideal prognostic biomarkers. However, whether lncRNAs are involved in m6A modification in colon adenocarcinoma (COAD) needs further exploration. The objective of this study was to construct an m6A-lncRNAs-based signature for patients with COAD. We obtained the RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA). Pearson correlation analysis was employed to recognize lncRNAs associated with m6A regulation (m6A-lncRNAs). 24 prognostic m6A-lncRNAs was identified by univariate Cox regression analysis. Gene set enrichment analysis (GSAE) was used to investigate the potential cellular pathways and biological processes. We have also explored the relationship between immune infiltrate levels and m6A-lncRNAs. Then, a predictive signature based on the expression of 13 m6A-lncRNAs was constructed by the Lasso regression algorithm, including UBA6-AS1, AC139149.1, U91328.1, AC138207.5, AC025171.4, AC008760.1, ITGB1-DT, AP001619.1, AL391422.4, AC104532.2, ZEB1-AS1, AC156455.1, and AC104819.3. ROC curves and K M survival curves have shown that the risk score has a well-predictive ability. We also set up a quantitative nomogram on the basis of risk score and prognosis-related clinical characteristics. In summary, we have identified some m6A-lncRNAs that correlated with prognosis and tumor immune microenvironment in COAD. In addition, a potential alternative signature based on the expression of m6A-lncRNAs was provided for the management of COAD patients.

BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks.

Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction.