scholarly journals GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Tao Duan ◽  
Zhufang Kuang ◽  
Jiaqi Wang ◽  
Zhihao Ma
Keyword(s):  
Heterogeneous Networks ◽  
Noncoding Rna ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Feature Learning ◽  
Structural Features ◽  
Disease Association ◽  
Gradient Boosting ◽  
Negative Sample ◽  
Disease Associations

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

scholarly journals miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Shen ◽  
You-Hua Zhang ◽  
Kyungsook Han ◽  
Asoke K. Nandi ◽  
Barry Honig ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Noncoding Rna ◽  
Esophageal Neoplasms ◽  
Kidney Neoplasms ◽  
Disease Association ◽  
Computational Method ◽  
Experimental Identification ◽  
Novel Mirna ◽  
Disease Associations ◽  
High Prediction

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

scholarly journals Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia Qu ◽  
Chun-Chun Wang ◽  
Shu-Bin Cai ◽  
Wen-Di Zhao ◽  
Xiao-Long Cheng ◽  
...  
Keyword(s):  
Random Walk ◽  
Heterogeneous Networks ◽  
Disease Association ◽  
Random Walk With Restart ◽  
Candidate Mirnas ◽  
Biased Random Walk ◽  
Disease Associations ◽  
And Performance ◽  
Function Similarity ◽  
Leave One Out

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.

scholarly journals Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination

Biomedicines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 124
Author(s):  
Jiakun Deng ◽  
Puying Tang ◽  
Xuegong Zhao ◽  
Tian Pu ◽  
Chao Qu ◽  
...  
Keyword(s):  
Local Structure ◽  
Characteristic Curve ◽  
Texture Features ◽  
Structural Features ◽  
Gradient Boosting ◽  
Initial Symptom ◽  
Feature Combination ◽  
Test Results ◽  
Structural Differences ◽  
Public Datasets

Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.

Hierarchical Extension Based on the Boolean Matrix for LncRNA-Disease Association Prediction

2020 ◽  
Vol 20 (6) ◽  
pp. 452-460
Author(s):  
Lin Tang ◽  
Yu Liang ◽  
Xin Jin ◽  
Lin Liu ◽  
Wei Zhou
Keyword(s):  
Computational Models ◽  
Characteristic Curve ◽  
Experimental Studies ◽  
Potential Method ◽  
Disease Association ◽  
Biological Data ◽  
Boolean Matrix ◽  
Disease Associations ◽  
Association Data ◽  
Association Matrix

Background: Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predicting LncRNA–disease associations based on computational models can save the huge cost of wet-lab experiments. Methods and Result: To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on the Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation based on the property of the Boolean matrix from various relational sources. Then, HEBLDA integrates these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA– disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross-validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Besides, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs. Conclusion: Based on its ability to discover the more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method that can obtain more comprehensive association prediction in a broad field.

scholarly journals A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

2020 ◽  
Author(s):  
Liyang Wang ◽  
Dantong Niu ◽  
Xiaoya Wang ◽  
Qun Shen ◽  
Yong Xue
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Bovine Milk ◽  
Structural Features ◽  
Protein Docking ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Antihypertensive Peptides

AbstractStrategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

2020 ◽  
Vol 36 (8) ◽  
pp. 2538-2546 ◽  
Author(s):  
Jin Li ◽  
Sai Zhang ◽  
Tao Liu ◽  
Chenxi Ning ◽  
Zhuoxuan Zhang ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Matrix Completion ◽  
Disease Association ◽  
Supplementary Information ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Feature Representations ◽  
Disease Similarity ◽  
Disease Associations ◽  
Association Data

Abstract Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA. Results We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs. Availability and implementation https://github.com/ljatynu/NIMCGCN/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xue-Jun Chen ◽  
Xin-Yun Hua ◽  
Zhen-Ran Jiang
Keyword(s):  
Target Genes ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Disease Associations ◽  
Version 2.0 ◽  
Practical Algorithm ◽  
Fold Cross Validation

Abstract Background A growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research. Results Inspired by the work of predecessors, we discover that the noise hiding in the data can affect the prediction performance and then propose an anti-noise algorithm (ANMDA) to predict potential miRNA-disease associations. Firstly, we calculate the similarity in miRNAs and diseases to construct features and obtain positive samples according to the Human MicroRNA Disease Database version 2.0 (HMDD v2.0). Then, we apply k-means on the undetected miRNA-disease associations and sample the negative examples equally from the k-cluster. Further, we construct several data subsets through sampling with replacement to feed on the light gradient boosting machine (LightGBM) method. Finally, the voting method is applied to predict potential miRNA-disease relationships. As a result, ANMDA can achieve an area under the receiver operating characteristic curve (AUROC) of 0.9373 ± 0.0005 in five-fold cross-validation, which is superior to several published methods. In addition, we analyze the predicted miRNA-disease associations with high probability and compare them with the data in HMDD v3.0 in the case study. The results show ANMDA is a novel and practical algorithm that can be used to infer potential miRNA-disease associations. Conclusion The results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise.

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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