A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction

In recent years, more and more studies have shown that miRNAs can affect a variety of biological processes. It is important for disease prevention, treatment, diagnosis, and prognosis to study the relationships between human diseases and miRNAs. However, traditional experimental methods are time-consuming and labour-intensive. Hence, in this paper, a novel neighborhood-based computational model called NBMDA is proposed for predicting potential miRNA-disease associations. Due to the fact that known miRNA-disease associations are very rare and many diseases (or miRNAs) are associated with only one or a few miRNAs (or diseases), in NBMDA, the K-nearest neighbor (KNN) method is utilized as a recommendation algorithm based on known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases to improve its prediction accuracy. And simulation results demonstrate that NBMDA can effectively infer miRNA-disease associations with higher accuracy compared with previous state-of-the-art methods. Moreover, independent case studies of esophageal neoplasms, breast neoplasms and colon neoplasms are further implemented, and as a result, there are 47, 48, and 48 out of the top 50 predicted miRNAs having been successfully confirmed by the previously published literatures, which also indicates that NBMDA can be utilized as a powerful tool to study the relationships between miRNAs and diseases.

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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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A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/6747453 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Haochen Zhao ◽

Linai Kuang ◽

Lei Wang ◽

Zhanwei Xuan

Keyword(s):

Computational Models ◽

State Of The Art ◽

Experimental Studies ◽

Research Field ◽

Computational Method ◽

Biological Processes ◽

Distance Correlation ◽

Novel Approach ◽

Disease Associations ◽

Leave One Out

Recently, accumulating laboratorial studies have indicated that plenty of long noncoding RNAs (lncRNAs) play important roles in various biological processes and are associated with many complex human diseases. Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.

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Text Classification Using Compression-Based Dissimilarity Measures

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415530043 ◽

2015 ◽

Vol 29 (05) ◽

pp. 1553004 ◽

Cited By ~ 7

Author(s):

David Pereira Coutinho ◽

Mário A. T. Figueiredo

Keyword(s):

Text Classification ◽

Nearest Neighbor ◽

State Of The Art ◽

Feature Space ◽

Machine Learning Algorithms ◽

Support Vector ◽

Feature Engineering ◽

Dissimilarity Measures ◽

Previous State ◽

Art Techniques

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

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BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

Complexity ◽

10.1155/2019/5938035 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Xiujuan Lei ◽

Wenxiang Zhang

Keyword(s):

Random Walk ◽

Heterogeneous Network ◽

State Of The Art ◽

Circular Rnas ◽

Biological Processes ◽

Biased Random Walk ◽

Random Walk Algorithm ◽

Disease Associations ◽

Walk Algorithm ◽

Better Than

The circular RNAs (circRNAs) have significant effects on a variety of biological processes, the dysfunction of which is closely related to the emergence and development of diseases. Therefore, identification of circRNA-disease associations will contribute to analysing the pathogenesis of diseases. Here, we present a computational model called BRWSP to predict circRNA-disease associations, which searches paths on a multiple heterogeneous network based on biased random walk. Firstly, BRWSP constructs a multiple heterogeneous network by using circRNAs, diseases, and genes. Then, the biased random walk algorithm runs on the multiple heterogeneous network to search paths between circRNAs and diseases. Finally, the performance of BRWSP is significantly better than the state-of-the-art algorithms. Furthermore, BRWSP further contributes to the discovery of novel circRNA-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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Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning

10.1101/2020.06.25.20140053 ◽

2020 ◽

Author(s):

Benjamin J. Lengerich ◽

Maruan Al-Shedivat ◽

Amir Alavi ◽

Jennifer Williams ◽

Sami Labbaki ◽

...

Keyword(s):

Deep Learning ◽

Predictive Accuracy ◽

State Of The Art ◽

Biological Processes ◽

Learning To Learn ◽

Treatment Protocols ◽

Lung Cancers ◽

Multiple Data ◽

Previous State ◽

Histopathology Images

AbstractWhen designing individualized treatment protocols for cancer patients, clinicians must synthesize the information from multiple data modalities into a single parsimonious description of the patient’s personal disease. However, such a description of a patient is never observed. In this work, we propose to model these patient descriptions as latent discriminative subtypes—sample representations which can be learned from one data modality and used to contextualize predictions based on another data modality. We apply contextual deep learning to learn these sample-specific discriminative subtypes from lung cancer histopathology imagery. Based on these subtypes, we produce sample-specific transcriptomic models which accurately classify samples as adenocarcinoma, squamous cell carcinoma, or healthy tissue (F1 score of 0.97, outperforming previous state-of-the-art multimodal approaches). Combining these data modalities in a single pipeline not only improves the predictive accuracy, but also gives biological interpretations of the discriminative subtypes and ties the phenotypic patterns present in histopathology images to biological processes.

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AEMDA: inferring miRNA–disease associations based on deep autoencoder

Bioinformatics ◽

10.1093/bioinformatics/btaa670 ◽

2020 ◽

Author(s):

Cunmei Ji ◽

Zhen Gao ◽

Xu Ma ◽

Qingwen Wu ◽

Jiancheng Ni ◽

...

Keyword(s):

Computational Models ◽

State Of The Art ◽

Disease Diagnosis ◽

Reconstruction Error ◽

Supplementary Information ◽

Biological Processes ◽

Interaction Data ◽

Computational Framework ◽

Disease Associations ◽

Non Coding Rnas

Abstract Motivation MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical roles in various biological processes. Many studies have shown that miRNAs are closely related to the occurrence, development and diagnosis of human diseases. Traditional biological experiments are costly and time consuming. As a result, effective computational models have become increasingly popular for predicting associations between miRNAs and diseases, which could effectively boost human disease diagnosis and prevention. Results We propose a novel computational framework, called AEMDA, to identify associations between miRNAs and diseases. AEMDA applies a learning-based method to extract dense and high-dimensional representations of diseases and miRNAs from integrated disease semantic similarity, miRNA functional similarity and heterogeneous related interaction data. In addition, AEMDA adopts a deep autoencoder that does not need negative samples to retrieve the underlying associations between miRNAs and diseases. Furthermore, the reconstruction error is used as a measurement to predict disease-associated miRNAs. Our experimental results indicate that AEMDA can effectively predict disease-related miRNAs and outperforms state-of-the-art methods. Availability and implementation The source code and data are available at https://github.com/CunmeiJi/AEMDA. Supplementary information Supplementary data are available at Bioinformatics online.

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DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion

Frontiers in Pharmacology ◽

10.3389/fphar.2021.784171 ◽

2022 ◽

Vol 12 ◽

Author(s):

Chu-Qiao Gao ◽

Yuan-Ke Zhou ◽

Xiao-Hong Xin ◽

Hui Min ◽

Pu-Feng Du

Keyword(s):

Computational Model ◽

State Of The Art ◽

Drug Repositioning ◽

Source Code ◽

Orphan Drugs ◽

Kernel Fusion ◽

Disease Associations ◽

Laplacian Regularized Least Squares ◽

Novel Drug ◽

Similarity Information

Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new computational model, DDA-SKF (drug–disease associations prediction using similarity kernels fusion), which can predict novel drug indications by utilizing similarity kernel fusion (SKF) and Laplacian regularized least squares (LapRLS) algorithms. DDA-SKF integrated multiple similarities of drugs and diseases. The prediction performances of DDA-SKF are better, or at least comparable, to all state-of-the-art methods. The DDA-SKF can work without sufficient similarity information between drug indications. This allows us to predict new purpose for orphan drugs. The source code and benchmarking datasets are deposited in a GitHub repository (https://github.com/GCQ2119216031/DDA-SKF).

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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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