scholarly journals Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Haixiu Yang ◽  
Fan Tong ◽  
Changlu Qi ◽  
Ping Wang ◽  
Jiangyu Li ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Disease Pathogenesis ◽  
Physiological Processes ◽  
Disease Associations ◽  
Inflammatory Bowel ◽  
Leave One Out ◽  
Insight Into

Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe–disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe–disease network by integrating known microbe–disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe–disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.

scholarly journals MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Da Xu ◽  
Hanxiao Xu ◽  
Yusen Zhang ◽  
Mingyi Wang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Least Squares ◽  
Cross Validation ◽  
Computational Method ◽  
Human Diseases ◽  
Regularized Least Squares ◽  
Comparison Results ◽  
Pathological Mechanism ◽  
Disease Associations ◽  
Inflammatory Bowel ◽  
Leave One Out

Abstract Background Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. Methods In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. Results The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. Conclusions All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.

scholarly journals Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoyu Yang ◽  
Linai Kuang ◽  
Zhiping Chen ◽  
Lei Wang
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Cosine Similarity ◽  
Disease Associations ◽  
Novel Method ◽  
Inflammatory Bowel ◽  
Leave One Out ◽  
Association Matrix ◽  
Similarity Matrices

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.

Adaptive boosting-based computational model for predicting potential miRNA-disease associations

2019 ◽  
Vol 35 (22) ◽  
pp. 4730-4738 ◽  
Author(s):  
Yan Zhao ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Human Diseases ◽  
Supplementary Information ◽  
Weak Classifier ◽  
Adaptive Boosting ◽  
Disease Associations ◽  
Leave One Out

AbstractMotivationRecent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems.ResultsDue to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures.Availability and implementationThe code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Evaluation of six methylation markers derived from genome-wide screens for detection of cervical precancer and cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (18) ◽  
pp. 1569-1578
Author(s):  
Stèfanie Dick ◽  
Lisanne Verhoef ◽  
Lise MA De Strooper ◽  
Iuliana Ciocănea-Teodorescu ◽  
G Bea A Wisman ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
High Performance ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Intraepithelial Neoplasia ◽  
Receiver Operating Characteristic Curves ◽  
Genome Wide ◽  
Cervical Precancer ◽  
Leave One Out

Aim: To evaluate the triage performance of six host-cell DNA methylation markers derived from two genome-wide discovery screens for detection of cervical precancer (cervical intraepithelial neoplasia 3 [CIN]) and cancer. Materials & methods: Human papillomavirus-positive cervical scrapes of controls (≤CIN1; n = 352) and women diagnosed with CIN3 (n = 175) or cervical cancer (n = 50) were analyzed for methylation of ASCL1, LHX8, ST6GALNAC5, GHSR, SST and ZIC1. Results: Methylation levels increased significantly with disease severity (all markers p < 0.001). Three markers ( ASCL1, LHX8, ZIC1) showed receiver operating characteristic curves with area under the curve >0.800 after leave-one-out cross-validation. Bi-marker panel ASCL1/LHX8 had highest area under the curve (0.882), and detected 83.4% of CIN3 and all cervical cancers at specificity of 82.4%. Conclusion: All six methylation markers showed an equivalent, high performance for the triage of human papillomavirus-positive women using cervical scrapes with complementarity between markers.

scholarly journals Sphingolipid Analysis Indicate Lactosylceramide as a Potential Biomarker of Inflammatory Bowel Disease in Children

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1083
Author(s):  
Aleksandra Filimoniuk ◽  
Agnieszka Blachnio-Zabielska ◽  
Monika Imierska ◽  
Dariusz Marek Lebensztejn ◽  
Urszula Daniluk
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
High Performance ◽  
Area Under The Curve ◽  
Study Groups ◽  
Diagnostic Value ◽  
Serum Samples ◽  
Specificity And Sensitivity ◽  
Potential Biomarker ◽  
Inflammatory Bowel

An altered ceramide composition in patients with inflammatory bowel disease (IBD) has been reported recently. The aim of this study was to evaluate the concentrations of sphingolipids in the serum of treatment-naive children with newly diagnosed IBD and to determine the diagnostic value of the tested lipids in pediatric IBD. The concentrations of sphingolipids in serum samples were evaluated using a quantitative method, an ultra-high-performance liquid chromatography-tandem mass spectrometry in children with Crohn’s disease (CD) (n=34), ulcerative colitis (UC) (n = 39), and controls (Ctr) (n = 24). Among the study groups, the most significant differences in concentrations were noted for C16:0-LacCer, especially in children with CD compared to Ctr or even to UC. Additionally, the relevant increase in C20:0-Cer and C18:1-Cer concentrations were detected in both IBD groups compared to Ctr. The enhanced C24:0-Cer level was observed only in UC, while C18:0-Cer only in the CD group. The highest area under the curve (AUC), specificity, and sensitivity were determined for C16:0-LacCer in CD diagnosis. Our results suggest that the serum LacC16-Cer may be a potential biomarker that distinguishes children with IBD from healthy controls and differentiates IBD subtypes. In addition, C20:0-Cer and C18:0-Cer levels also seem to be closely connected with IBD.

Insight into Inflammatory Bowel Disease Pathogenesis: Is the Answer Blowing in the Wind?

2021 ◽  
Author(s):  
Sari M Herman ◽  
Karver Zaborniak ◽  
Charles N Bernstein
Keyword(s):  
Ulcerative Colitis ◽  
Atopic Disease ◽  
Immune Dysregulation ◽  
Potential Contribution ◽  
Food Allergens ◽  
Disease Pathogenesis ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Immunomodulatory Therapies ◽  
Insight Into

Abstract Inflammatory bowel diseases (IBD) including Crohn’s disease and ulcerative colitis are conditions characterized by immune dysregulation to a trigger in those with a genetic predisposition. Environmental factors are thought to contribute to IBD, but no definite trigger has been identified. Aeroallergens have not been thoroughly investigated in their potential contribution to the pathogenesis to IBD. The geographic distribution of aeroallergens and IBD, the association of atopic disease with IBD, seasonality and IBD, and cross-reactive food allergens require further study with implications for targeted dietary and immunomodulatory therapies.

scholarly journals WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Guobo Xie ◽  
Zhiliang Fan ◽  
Yuping Sun ◽  
Cuiming Wu ◽  
Lei Ma
Keyword(s):  
Cross Validation ◽  
Lung Neoplasm ◽  
Disease Association ◽  
Computational Techniques ◽  
Bipartite Network ◽  
Initial Information ◽  
Method Transfer ◽  
Disease Associations ◽  
Association Data ◽  
Leave One Out

Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173 ± 0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.

scholarly journals Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

Cells ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 881 ◽  
Author(s):  
Jihwan Ha ◽  
Chihyun Park ◽  
Chanyoung Park ◽  
Sanghyun Park
Keyword(s):  
Computational Models ◽  
Matrix Completion ◽  
Area Under The Curve ◽  
Excellent Performance ◽  
Novel Mirna ◽  
Lack Of Information ◽  
Disease Associations ◽  
Roc Area ◽  
Leave One Out ◽  
Global And Local

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

scholarly journals LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

2020 ◽  
Vol 21 (4) ◽  
pp. 1508
Author(s):  
Yi Zhang ◽  
Min Chen ◽  
Ang Li ◽  
Xiaohui Cheng ◽  
Hong Jin ◽  
...  
Keyword(s):  
Computational Methods ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Weighted Networks ◽  
Disease Associations ◽  
Space Projection ◽  
Auc Value ◽  
Leave One Out ◽  
Key Steps

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

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