scholarly journals SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dayun Liu ◽  
Yibiao Huang ◽  
Wenjuan Nie ◽  
Jiaxuan Zhang ◽  
Lei Deng
Keyword(s):  
Molecular Level ◽  
Small Scale ◽  
Computational Framework ◽  
Stacked Autoencoder ◽  
Disease Mechanisms ◽  
Disease Associations ◽  
Auc Value ◽  
Carcinoma Colon ◽  
Association Matrix ◽  
Validation Experiments

Abstract Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

scholarly journals MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Factorization Method ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix

Abstract Background MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). Conclusions The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jianwei Li ◽  
Jianing Li ◽  
Mengfan Kong ◽  
Duanyang Wang ◽  
Kun Fu ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Molecular Level ◽  
Singular Value ◽  
Linear Features ◽  
Disease Mechanisms ◽  
Non Linear ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Value Decomposition ◽  
Novel Model

Abstract Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. Results In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. Conclusions We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.

scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals Prediction of lncRNA–Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

2022 ◽  
Vol 12 ◽  
Author(s):  
Jianwei Li ◽  
Mengfan Kong ◽  
Duanyang Wang ◽  
Zhenwu Yang ◽  
Xiaoke Hao
Keyword(s):  
Molecular Level ◽  
Recognition Problem ◽  
Great Success ◽  
Convolutional Network ◽  
Disease Associations ◽  
Auc Value ◽  
Non Coding Rnas ◽  
Node Weight ◽  
Human Complex ◽  
Disease Characteristic

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

Recent advances in multivalent carbon nanoform-based glycoconjugates

2021 ◽  
Vol 28 ◽  
Author(s):  
Javier Ramos-Soriano ◽  
Mattia Ghirardello ◽  
M. Carmen Galan
Keyword(s):  
Carbon Nanotubes ◽  
Molecular Level ◽  
Biological Processes ◽  
Biologically Relevant ◽  
Disease Mechanisms ◽  
Recent Advances

: Multivalent carbohydrate-mediated interactions are fundamental to many biological processes, including disease mechanisms. To study these significant glycan-mediated interactions at a molecular level, carbon nanoforms such as fullerenes, carbon nanotubes, or graphene and their derivatives have been identified as promising biocompatible scaffolds that can mimic the multivalent presentation of biologically relevant glycans. In this minireview, we will summarize the most relevant examples of the last few years in the context of their applications.

Microstructural Size Dependency in Nonlinear Lateral Stability of Random Reinforced Microshells via Meshfree-Based Applied Mathematical Modeling

2021 ◽  
pp. 2150164
Author(s):  
Jie-Hua Sun ◽  
Zhi-Dong Zhou ◽  
Saeid Sahmani ◽  
Babak Safaei
Keyword(s):  
Research Work ◽  
Meshfree Method ◽  
Resin Matrix ◽  
Small Scale ◽  
Lateral Stability ◽  
Computational Framework ◽  
Probabilistic Technique ◽  
Prime Objective ◽  
Shear Deformable

The prime objective of this research work is to develop an efficient small scale-dependent computational framework incorporating microstructural tensors of dilatation gradient, rotation gradient, and deviatoric stretch gradient to analyze nonlinear lateral stability of cylindrical microshells. The numerical strategy is established based upon a mixed formation of the third-order shear deformable shell model and modified strain gradient continuum mechanics. The graphene nanoplatelet reinforcements are assumed to be randomly dispersed in a checkerboard scheme within the resin matrix. Accordingly, to extract the effective material properties, the Monte Carlo simulation together with a probabilistic technique are employed. The numerical solution for the microstructural-dependent nonlinear problem is carried out via the moving Kriging meshfree method having the capability to accommodate accurately the essential boundary conditions using proper moving Kriging shape function. It is represented that the role of the stiffening characters related to the effect of microstructural dilatation gradient, rotation gradient, and deviatoric stretch reduces continuously by going to deeper territory of the load-deflection stability path. Moreover, it is indicated that among various microstructural gradient tensors, the stiffening character of the rotation gradient is higher than deviatoric stretch gradient, and the stiffening character of the latter is more considerable than the dilatation gradient tensor.

scholarly journals Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wang ◽  
Jing Zhang
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cancer Survival ◽  
Disease Association ◽  
The Body ◽  
Survival Prediction ◽  
Clinical Prognosis ◽  
Disease Associations ◽  
Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

scholarly journals DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 608 ◽  
Author(s):  
Yan Li ◽  
Junyi Li ◽  
Naizheng Bian
Keyword(s):  
Matrix Factorization ◽  
Disease Diagnosis ◽  
Disease Association ◽  
Bayesian Optimization ◽  
Model Parameters ◽  
Target Interaction ◽  
Disease Associations ◽  
Auc Value ◽  
Similarity Networks ◽  
Fold Cross Validation

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.

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