scholarly journals WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen-Wei Zhang ◽  
Zhen Gao ◽  
Chun-Hou Zheng ◽  
Lei Li ◽  
Su-Min Qi ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Cross Validation ◽  
Mirna Expression Profile ◽  
Disease Association ◽  
Weighted Voting ◽  
Noise Interference ◽  
Proposed Model ◽  
Incomplete Similarity ◽  
Similarity Information ◽  
Validation Experiments

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.

scholarly journals GCNCDA: A New Method for Predicting CircRNA-Disease Associations Based on Graph Convolutional Network Algorithm

2019 ◽  
Author(s):  
Lei Wang ◽  
Zhu-Hong You ◽  
Yang-Ming Li ◽  
Kai Zheng ◽  
Yu-An Huang
Keyword(s):  
Feature Extraction ◽  
Computational Methods ◽  
Cross Validation ◽  
Complex Diseases ◽  
Disease Association ◽  
Diagnosis And Treatment ◽  
Biological Experiment ◽  
Validation Experiment ◽  
Disease Associations ◽  
Similarity Information

AbstractNumerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, 10 of the top 15 circRNA-disease association candidates with the highest prediction scores were confirmed by recently published literature. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments.Author SummaryThe recognition of circRNA-disease association is the key of disease diagnosis and treatment, and it is of great significance for exploring the pathogenesis of complex diseases. Computational methods can predicte the potential disease-related circRNAs quickly and accurately. Based on the hypothesis that circRNA with similar function tends to associate with similar disease, GCNCDA model is proposed to effectively predict the potential association between circRNAs and diseases by combining FastGCN algorithm. The performance of the model was verified by cross-validation experiments, different feature extraction algorithm and classifier models comparison experiments. Furthermore, 10 of the top 15 disease-associated circRNAs with the highest prediction scores were confirmed by recently published literature. It is anticipated that GCNCDA model can give priority to the most promising circRNA-disease associations on a large scale to provide reliable candidates for further biological experiment.

scholarly journals Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm

2020 ◽  
Vol 7 ◽  
Author(s):  
Xiujuan Lei ◽  
Cheng Zhang ◽  
Yueyue Wang
Keyword(s):  
Artificial Bee Colony ◽  
Cross Validation ◽  
Predictive Performance ◽  
Disease Association ◽  
Biomedical Domain ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Disease Associations ◽  
Novel Method ◽  
Validation Experiments

In recent years, latent metabolite-disease associations have been a significant focus in the biomedical domain. And more and more experimental evidence has been adduced that metabolites correlate with the diagnosis of complex human diseases. Several computational methods have been developed to detect potential metabolite-disease associations. In this article, we propose a novel method based on the spy strategy and an artificial bee colony (ABC) algorithm for metabolite-disease association prediction (SSABCMDA). Due to the fact that there are large parts of missing associations in unconfirmed metabolite-disease pairs, spy strategy is adopted to extract reliable negative samples from unconfirmed pairs. Considering the effects of parameters, the ABC algorithm is utilized to optimize parameters. In relevant cross-validation experiments, our method achieves excellent predictive performance. Moreover, three types of case studies are conducted on three common diseases to demonstrate the validity and utility of SSABCMDA method. Relevant experimental results indicate that our method can predict potential associations between metabolites and diseases effectively.

iTSP-PseAAC: Identify Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC

2021 ◽  
Vol 15 ◽  
Author(s):  
Muhammad Awais ◽  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser Daanial Khan
Keyword(s):  
Tumor Suppressor ◽  
Cross Validation ◽  
Control Cell ◽  
Normal Function ◽  
In Vivo Studies ◽  
Tumor Suppressor Proteins ◽  
Proposed Model ◽  
Self Consistency

Background: The uncontrolled growth due to accumulation of genetic and epigenetic changes as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Pro-oncogenes is known as cancer. TSGs control cell division and growth by repairing of DNA mistakes during replication and restrict the unwanted proliferation of a cell or activities, those are the part of tumor production. Objectives: This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor proteins, which would be freely available to experimental molecular biologists to assist them using in vitro and in vivo studies. Methods: The predictor model has used the input feature vector (IFV) calculated from the physicochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Results: Using self-consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. Conclusion: It concludes, the proposed model for prediction of the tumor suppressor proteins can predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

scholarly journals Interclass Interference Suppression in Multi-Class Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 450
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Na Jiang ◽  
Ran Cheng ◽  
Xianling Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Validation ◽  
Selection Process ◽  
Interference Suppression ◽  
Generalization Ability ◽  
Suppression Effect ◽  
Binary Classifiers ◽  
The One ◽  
Fold Cross Validation ◽  
Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

Abstract WP319: miRNA Screening of Natural Killer Cell Identifies miR-1224 as a Post-Transcriptional Regulator of NK Cell Function After Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Yan Feng ◽  
Hui Zhao ◽  
Fu-Dong Shi ◽  
Weina Jin
Keyword(s):  
Ischemic Stroke ◽  
Cerebral Ischemia ◽  
Nk Cells ◽  
Expression Profile ◽  
Mirna Expression ◽  
Cell Function ◽  
Nk Cell ◽  
Killer Cell ◽  
Mirna Expression Profile ◽  
Control Group

Objectives: To screen miRNA profile of peripheral NK cells in ischemic stroke mouse model and investigate a most promising candidate (miR-1224) for post-transcriptional regulation of NK cell function after ischemic stroke. Methods: Mice were subjected to a 60 min focal cerebral ischemia produced by transient intraluminal occlusion of MCAO. For NK cell isolation, cell suspensions from the spleens after reperfusion were enriched for NK cells using magnetic-bead sorting system after staining with anti-NK1.1 microbeads. The nCounter Mouse miRNA array was used to analyze miRNA expression profile in splenic NK cells over the time course of experimental ischemic stroke. Based on the miRNA data, we further in vitro modulated miR-1224 in NK cells using mimics or inhibitor, then injected i.v into Rag2-/-γc-/- recipient mice. Neurological function score was compared and spontaneous infection was assessed by pulmonary bacteria colony culture, and changes in potential signaling pathway (SP1/TNF-α) were verified by rt-PCR and western blot. Results: Through miRNA expression profile analysis, we have identified significant changes at each time point in peripheral NK cells after cerebral ischemia. Among all screened miRNA, miR-1224 remarkably increased in MCAO group, which was verified by PCR. Then isolated NK cells treated with mimics or inhibitors, were transferred to Rag2-/-γc-/- recipient mice. Compared with WT mice, Rag2-/-γc-/- mice with miR-1224 inhibitor exhibited increased NK cell number, enhanced NK cell activation/cytotoxicity feature, as well as better neurological behaviors and reduced pulmonary infection after MCAO. Moreover, compared with the control group, NK cells with miR-1224 inhibitor showed significantly increased SP1 gene and protein phosphorylation. As SP1 gene is one of the potential targets of miR-1224, this study suggests that miR-1224 may regulate NK cell function after MCAO, which is associated with SP1 pathway. Conclusion: The miRNA profiling of splenic NK cells provided insight into the functional mechanism and signaling pathways underlying the distinct organ-specific NK cell properties, which will contribute to the better understanding of NK cell mediated immune-response in relation to different stages of stroke.

scholarly journals Global miRNA expression profile reveals novel molecular players in aneurysmal subarachnoid haemorrhage

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Katia de Paiva Lopes ◽  
Tatiana Vinasco-Sandoval ◽  
Ricardo Assunção Vialle ◽  
Fernando Mendes Paschoal ◽  
Vanessa Albuquerque P. Aviz Bastos ◽  
...  
Keyword(s):  
Subarachnoid Haemorrhage ◽  
Expression Profile ◽  
Mirna Expression ◽  
Mirna Expression Profile ◽  
Aneurysmal Subarachnoid Haemorrhage

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

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