Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis

Pallez, Denis; Gardès, Julien; Pasquier, Claude

doi:10.1038/s41598-017-10065-y

Cited by 13 publications

(7 citation statements)

References 79 publications

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“…Considering some existing methods have taken advantage of different datasets to identify miRNA-disease associations, which makes direct comparison of their performance and the performance of the proposed method is not realistic. For example, two model proposed by Pallez et al ( 2017 ) and Pasquier and Gardès ( 2016 ) were based on the dataset of miRNA-disease associations, miRNA-neighbor associations, miRNA-target associations, miRNA-word associations and miRNA-family associations. The model proposed by Mork et al ( 2014 ) was based on the dataset of miRNA–protein associations and protein-disease associations to predict potential miRNA-disease associations.…”

Section: Discussionmentioning

confidence: 99%

“…Zou et al ( 2015 ) introduced two computational methods of KATZ and CATAPULT to make prediction for miRNA-disease pairs based on social network analysis methods. Pallez et al ( 2017 ) presented a predictive approach named MiRAI using an evolutionary tuned latent semantic analysis. Pasquier and Gardès ( 2016 ) make prediction for miRNA-disease associations with a vector space model.…”

Section: Introductionmentioning

confidence: 99%

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TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

Chen

Yin

2018

Front. Genet.

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In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

Chen

Yin

2018

Front. Genet.

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“…Identifying the associations between a pair of miRNA and disease/phenotype via biological experiment is time-consuming and costly in terms of financial input. Therefore, researchers explore to predict these associations automatically based on computational approaches [Pallez et al, 2017, Zou et al, 2015, You et al, 2017. Most of existing approaches are based on network.…”

Section: Introductionmentioning

confidence: 99%

A learning-based framework for miRNA-disease association identification using neural networks

et al. 2019

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Motivation A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes. Results We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction. Availability and implementation The source code and data are available at https://github.com/Issingjessica/MDA-CNN. Supplementary information Supplementary data are available at Bioinformatics online.

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“…The majority of proteins work in protein complexes and proteins that interact with one another are often found to be involved in the same cellular processes, making the study of protein-protein interactions (PPIs) more relevant, namely in protein function and drug target prediction, as well as in disease research [5]- [7]. Due to their regulatory role in important cellular functions, such as metabolism and gene regulation, the interest in the study of miRNAs involvement in disease as biomarkers has recently increased [8], [9]. Consequently, the study of integrated networks consisting of protein-protein interaction networks (PPINs) and miRNAtarget interaction networks (MTIs) is likely to provide insights into MD genes.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction

Cecchini

Nguyen

Pfau

et al. 2019

2019 11th International Conference on Knowledge and Systems Engineering (KSE)

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The increase of obesity, its related diseases and the high incidence of metabolic diseases as a whole, constitute a major public health problem on a global scale. New strategies that allow for the discovery of novel metabolic disease-related genes are necessary to develop new treatments. In this paper, we proposed an efficient method to predict metabolic disease genes, solving the problem of imbalanced data. The method combined protein-protein interactions and miRNA-target interactions to construct integrated networks, whose topological properties can be used as features to train machine learning classifiers. We applied different strategies to optimize imbalanced class. The best model of gradient boosting achieved a significant F1-score of 0.82. When testing the model with non-disease genes, we predicted 549 candidates, out of which 123 were validated indirectly from literature to be related to metabolic diseases. The remaining genes' functions were investigated by gene enrichment analysis, revealing their association with diseases known to co-occur with metabolic diseases, such as cancer and cardiovascular conditions. These results indicated that this method contributed to the identification of novel metabolic disease-related genes.

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Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis

Cited by 13 publications

References 79 publications

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

A learning-based framework for miRNA-disease association identification using neural networks

An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction

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