scholarly journals Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1040 ◽  
Author(s):  
Li Zhang ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Operating Characteristics ◽  
Learning Framework ◽  
Disease Similarity ◽  
Variational Autoencoder ◽  
Disease Associations ◽  
Unsupervised Deep Learning ◽  
Leave One Out

The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature.

Identifying human microRNA–disease associations by a new diffusion-based method

2015 ◽  
Vol 13 (04) ◽  
pp. 1550014 ◽  
Author(s):  
Bo Liao ◽  
Sumei Ding ◽  
Haowen Chen ◽  
Zejun Li ◽  
Lijun Cai
Keyword(s):  
Biomedical Research ◽  
Prediction Accuracy ◽  
Information Sources ◽  
Cross Validation ◽  
Disease Association ◽  
Global Network ◽  
Disease Similarity ◽  
Disease Associations ◽  
Network Similarity ◽  
Leave One Out

Identifying the microRNA–disease relationship is vital for investigating the pathogenesis of various diseases. However, experimental verification of disease-related microRNAs remains considerable challenge to many researchers, particularly for the fact that numerous new microRNAs are discovered every year. As such, development of computational methods for disease-related microRNA prediction has recently gained eminent attention. In this paper, first, we construct a miRNA functional network and a disease similarity network by integrating different information sources. Then, we further introduce a new diffusion-based method (NDBM) to explore global network similarity for miRNA–disease association inference. Even though known miRNA–disease associations in the database are rare, NDBM still achieves an area under the ROC curve (AUC) of 85.62% in the leave-one-out cross-validation in improving the prediction accuracy of previous methods significantly. Moreover, our method is applicable to diseases with no known related miRNAs as well as new miRNAs with unknown target diseases. Some associations who strongly predicted by our method are confirmed by public databases. These superior performances suggest that NDBM could be an effective and important tool for biomedical research.

scholarly journals Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework

2021 ◽  
Vol 33 (8) ◽  
pp. 086108
Author(s):  
Jing Wang ◽  
Cheng He ◽  
Runze Li ◽  
Haixin Chen ◽  
Chen Zhai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Field ◽  
Learning Framework ◽  
Variational Autoencoder

scholarly journals WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Guobo Xie ◽  
Zhiliang Fan ◽  
Yuping Sun ◽  
Cuiming Wu ◽  
Lei Ma
Keyword(s):  
Cross Validation ◽  
Lung Neoplasm ◽  
Disease Association ◽  
Computational Techniques ◽  
Bipartite Network ◽  
Initial Information ◽  
Method Transfer ◽  
Disease Associations ◽  
Association Data ◽  
Leave One Out

Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173 ± 0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.

scholarly journals Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

2021 ◽  
Vol 151 ◽  
pp. 267-274
Author(s):  
Romany F. Mansour ◽  
José Escorcia-Gutierrez ◽  
Margarita Gamarra ◽  
Deepak Gupta ◽  
Oscar Castillo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Variational Autoencoder ◽  
Unsupervised Deep Learning ◽  
Diagnosis And Classification

scholarly journals ON THE USE OF MULTIPLE INSTANCE LEARNING FOR DATA CLASSIFICATION

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-127-1-137
Author(s):  
Nibras Z. Salih ◽  
◽  
Walaa Khalaf ◽  
Keyword(s):  
Cross Validation ◽  
Feature Vector ◽  
Multiple Instance Learning ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Bayes Classifier ◽  
Learning Framework ◽  
Single Feature ◽  
Single Instance ◽  
Leave One Out

In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning. Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results. A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning. The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75%, whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33%.

Deep-belief network for predicting potential miRNA-disease associations

2020 ◽  
Author(s):  
Xing Chen ◽  
Tian-Hao Li ◽  
Yan Zhao ◽  
Chun-Chun Wang ◽  
Chi-Chi Zhu
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Biological Data ◽  
Deep Belief Network ◽  
Computational Method ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Disease Associations ◽  
Leave One Out ◽  
The Impact

Abstract MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

scholarly journals Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Quan Zou ◽  
Jinjin Li ◽  
Qingqi Hong ◽  
Ziyu Lin ◽  
Yun Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cross Validation ◽  
Supervised Machine Learning ◽  
Rna Molecules ◽  
Disease Associations ◽  
Endogenous Genes ◽  
Leave One Out

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

scholarly journals MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Da Xu ◽  
Hanxiao Xu ◽  
Yusen Zhang ◽  
Mingyi Wang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Least Squares ◽  
Cross Validation ◽  
Computational Method ◽  
Human Diseases ◽  
Regularized Least Squares ◽  
Comparison Results ◽  
Pathological Mechanism ◽  
Disease Associations ◽  
Inflammatory Bowel ◽  
Leave One Out

Abstract Background Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. Methods In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. Results The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. Conclusions All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.

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