Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosis and treatment of human diseases. The interactions between miRNA and human disease have rarely been demonstrated, and the underlying mechanism of miRNA is not clear. Therefore, computational approaches has attracted the attention of researchers, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under five-fold cross validation (five-fold CV) showed that HGATMDA achieved better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for the three diseases mentioned above, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool to identity potential diseases-related miRNAs.

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A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

BMC Bioinformatics ◽

10.1186/s12859-020-03906-7 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

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A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

10.21203/rs.2.18937/v3 ◽

2020 ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

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Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

BMC Bioinformatics ◽

10.1186/s12859-021-04486-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Feng Zhou ◽

Meng-Meng Yin ◽

Cui-Na Jiao ◽

Zhen Cui ◽

Jing-Xiu Zhao ◽

...

Keyword(s):

Bipartite Graph ◽

Matrix Factorization ◽

Cross Validation ◽

Rapid Development ◽

Factorization Method ◽

Computational Method ◽

Human Diseases ◽

Simulation Experiments ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

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LOMDA: Linear optimization for miRNA-disease association prediction

10.1101/751651 ◽

2019 ◽

Author(s):

Yan-Li Lee ◽

Ratha Pech ◽

Maryna Po ◽

Dong Hao ◽

Tao Zhou

Keyword(s):

Cross Validation ◽

Linear Optimization ◽

Optimization Technique ◽

Auxiliary Information ◽

Prediction Performance ◽

First Case ◽

Disease Associations ◽

Similarity Information ◽

Fold Cross Validation

AbstractMicroRNAs (miRNAs) have been playing a crucial role in many important biological processes e.g., pathogenesis of diseases. Currently, the validated associations between miRNAs and diseases are insufficient comparing to the hidden associations. Testing all these hidden associations by biological experiments is expensive, laborious, and time consuming. Therefore, computationally inferring hidden associations from biological datasets for further laboratory experiments has attracted increasing interests from different communities ranging from biological to computational science. In this work, we propose an effective and efficient method to predict associations between miRNAs and diseases, namely linear optimization (LOMDA). The proposed method uses the heterogenous matrix incorporating of miRNA functional similarity information, disease similarity information and known miRNA-disease associations. Compared with the other methods, LOMDA performs best in terms of AUC (0.970), precision (0.566), and accuracy (0.971) in average over 15 diseases in local 5-fold cross-validation. Moreover, LOMDA has also been applied to two types of case studies. In the first case study, 30 predictions from breast neoplasms, 24 from colon neoplasms, and 26 from kidney neoplasms among top 30 predicted miRNAs are confirmed. In the second case study, for new diseases without any known associations, top 30 predictions from hepatocellular carcinoma and 29 from lung neoplasms among top 30 predicted miRNAs are confirmed.Author summaryIdentifying associations between miRNAs and diseases is significant in investigation of pathogenesis, diagnosis, treatment and preventions of related diseases. Employing computational methods to predict the hidden associations based on known associations and focus on those predicted associations can sharply reduce the experimental costs. We developed a computational method LOMDA based on the linear optimization technique to predict the hidden associations. In addition to the observed associations, LOMDA also can employ the auxiliary information (diseases and miRNAs similarity information) flexibly and effectively. Numerical experiments on global 5-fold cross validation show that the use of the auxiliary information can greatly improve the prediction performance. Meanwhile, the result on local 5-fold cross validation shows that LOMDA performs best among the seven related methods. We further test the prediction performance of LOMDA for two types of diseases based on HDMMv2.0 (2014), including (i) diseases with all the known associations, and (ii) new diseases without known associations. Three independent or updated databases (dbDEMC, 2010; miR2Disease, 2009; HDMMv3.2, 2019) are introduced to evaluate the prediction results. As a result, most miRNAs for target diseases are confirmed by at least one of the three databases. So, we believe that LOMDA can guide experiments to identify the hidden miRNA-disease associations.

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GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009655 ◽

2021 ◽

Vol 17 (12) ◽

pp. e1009655

Author(s):

Lei Li ◽

Yu-Tian Wang ◽

Cun-Mei Ji ◽

Chun-Hou Zheng ◽

Jian-Cheng Ni ◽

...

Keyword(s):

Cross Validation ◽

State Of The Art ◽

Human Diseases ◽

Biological Processes ◽

Proposed Model ◽

Disease Associations ◽

Convolutional Autoencoder ◽

Non Coding Rnas ◽

Novel Model ◽

Better Than

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolution autoencoder (GCAE) is utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performance were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA were beneficial to infer potential associations of miRNA-disease.

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Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

BioMed Research International ◽

10.1155/2021/6652948 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Li Wang ◽

Cheng Zhong

Keyword(s):

Collaborative Filtering ◽

Cross Validation ◽

Kidney Neoplasms ◽

Feature Vector ◽

High Failure Rate ◽

Experimental Identification ◽

Disease Similarity ◽

Disease Associations ◽

Novel Method ◽

Fold Cross Validation

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.

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A Novel Approach Based on Point Cut Set to Predict Associations of Diseases and LncRNAs

Current Bioinformatics ◽

10.2174/1574893613666181026122045 ◽

2019 ◽

Vol 14 (4) ◽

pp. 333-343 ◽

Cited By ~ 3

Author(s):

Linai Kuang ◽

Haochen Zhao ◽

Lei Wang ◽

Zhanwei Xuan ◽

Tingrui Pei

Keyword(s):

Cross Validation ◽

State Of The Art ◽

Interaction Network ◽

Research Field ◽

Computational Method ◽

Difference Matrix ◽

Art Methods ◽

Disease Associations ◽

Cut Set ◽

Fold Cross Validation

Background: In recent years, more evidence have progressively indicated that Long non-coding RNAs (lncRNAs) play vital roles in wide-ranging human diseases, which can serve as potential biomarkers and drug targets. Comparing with vast lncRNAs being found, the relationships between lncRNAs and diseases remain largely unknown. Objective: The prediction of novel and potential associations between lncRNAs and diseases would contribute to dissect the complex mechanisms of disease pathogenesis. associations while known disease-lncRNA associations are required only. Method: In this paper, a new computational method based on Point Cut Set is proposed to predict LncRNA-Disease Associations (PCSLDA) based on known lncRNA-disease associations. Compared with the existing state-of-the-art methods, the major novelty of PCSLDA lies in the incorporation of distance difference matrix and point cut set to set the distance correlation coefficient of nodes in the lncRNA-disease interaction network. Hence, PCSLDA can be applied to forecast potential lncRNAdisease associations while known disease-lncRNA associations are required only. Results: Simulation results show that PCSLDA can significantly outperform previous state-of-the-art methods with reliable AUC of 0.8902 in the leave-one-out cross-validation and AUCs of 0.7634 and 0.8317 in 5-fold cross-validation and 10-fold cross-validation respectively. And additionally, 70% of top 10 predicted cancer-lncRNA associations can be confirmed. Conclusion: It is anticipated that our proposed model can be a great addition to the biomedical research field.

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A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

10.21203/rs.2.18937/v2 ◽

2020 ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is helpful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Moreover, comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

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WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

BMC Bioinformatics ◽

10.1186/s12859-019-3066-0 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 4

Author(s):

Yahui Long ◽

Jiawei Luo

Keyword(s):

Computational Methods ◽

Cross Validation ◽

Search Algorithm ◽

Human Diseases ◽

Information Network ◽

Bipartite Network ◽

Heterogeneous Information Network ◽

Similarity Network ◽

Heterogeneous Information ◽

Disease Associations

Abstract Background An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases. Results Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores. Conclusions The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn’s disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

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Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs

International Journal of Molecular Sciences ◽

10.3390/ijms19103052 ◽

2018 ◽

Vol 19 (10) ◽

pp. 3052 ◽

Cited By ~ 7

Author(s):

Bi Zhao ◽

Bin Xue

Keyword(s):

Cross Validation ◽

Prediction Performance ◽

Computational Techniques ◽

Binding Motifs ◽

Intrinsically Disordered ◽

Biological Studies ◽

Independent Test ◽

Overall Performance ◽

Improved Accuracy ◽

Fold Cross Validation

Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for improvement. Among many possibilities, a meta-strategy that integrates the results of multiple individual predictors has been broadly used to improve the overall performance of predictors. Nonetheless, a simple and direct integration of individual predictors may not effectively improve the performance. In this project, dual-threshold two-step significance voting and neural networks were used to integrate the predictive results of four individual predictors, including: DisEMBL, IUPred, VSL2, and ESpritz. The new meta-strategy has improved the prediction performance of intrinsically disordered residues significantly, compared to all four individual predictors and another four recently-designed predictors. The improvement was validated using five-fold cross-validation and in independent test datasets.

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