Dataset Information

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

ABSTRACT: Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA?disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA?disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA?disease associations (WINMDA) by integrating the T most similar neighbors and the shortest path algorithm. Simulation results show that WINMDA can achieve reliable area under the receiver operating characteristics (ROC) curve (AUC) results of 0.9183 ± 0.0007 in 5-fold cross-validation, 0.9200 ± 0.0004 in 10-fold cross-validation, 0.9243 in global leave-one-out cross-validation (LOOCV), and 0.8856 in local LOOCV. Furthermore, case studies of colon neoplasms, gastric neoplasms, and prostate neoplasms based on the Human microRNA Disease Database (HMDD) database were implemented, for which 94% (colon neoplasms), 96% (gastric neoplasms), and 96% (prostate neoplasms) of the top 50 predicting miRNAs were confirmed by recent experimental reports, which also demonstrates that WINMDA can effectively uncover potential miRNA?disease associations.

SUBMITTER: Zhao H

PROVIDER: S-EPMC6337518 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

Zhao Haochen H Kuang Linai L Feng Xiang X Zou Quan Q Wang Lei L

International journal of molecular sciences 20181228 1

Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA⁻disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA⁻disease associations, as well as the integrated similarity betwee ...[more]

PMID: 30597923

Similar Datasets

Project description:The current global pandemic, Covid-19, is a severe threat to human health and existence especially when it is mutating very frequently. Being a novel disease, Covid-19 is impacting the patients with comorbidities and is predicted to have long-term consequences, even for those who have recovered from it. To clearly recognize its impact, it is important to comprehend the complex relationship between Covid-19 and other diseases. It is also being observed that people with good immune system are less susceptible to the disease. It is perceived that if a correlation between Covid-19, other diseases, and diet is realized, then caregivers would be able to enhance their further course of medical action and recommendations. Network Analysis is one such technique that can bring forth such complex interdependencies and associations. In this paper, a Network Analysis-based approach has been proposed for analyzing the interplay of diets/foods along with Covid-19 and other diseases. Relationships between Covid-19, diabetes mellitus type 2 (T2DM), non-alcoholic fatty liver disease (NAFLD), and diets have been curated, visualized, and further analyzed in this study so as to predict unknown associations. Network algorithms including Louvain graph algorithm (LA), K nearest neighbors (KNN), and Page rank algorithms (PR) have been employed for predicting a total of 60 disease-diet associations, out of which 46 have been found to be either significant in disease risk prevention/mitigation or in its progression as validated using PubMed literature. A precision of 76.7% has been achieved which is significant considering the involvement of a novel disease like Covid-19. The generated interdependencies can be further explored by medical professionals and caregivers in order to plan healthy eating patterns for Covid-19 patients. The proposed approach can also be utilized for finding beneficial diets for different combinations of comorbidities with Covid-19 as per the underlying health conditions of a patient. Graphical abstract.

Project description:Background MicroRNAs (miRNAs) have been confirmed to be inextricably linked to the emergence of human complex diseases. The identification of the disease-related miRNAs has gradually become a routine way to unveil the genetic mechanisms of examined disorders. Methods In this study, a method BLNIMDA based on a weighted bi-level network was proposed for predicting hidden associations between miRNAs and diseases. For this purpose, the known associations between miRNAs and diseases as well as integrated similarities between miRNAs and diseases are mapped into a bi-level network. Based on the developed bi-level network, the miRNA-disease associations (MDAs) are defined as strong associations, potential associations and no associations. Then, each miRNA-disease pair (MDP) is assigned two information properties according to the bidirectional information distribution strategy, i.e., associations of miRNA towards disease and vice-versa. Finally, two affinity weights for each MDP obtained from the information properties and the association type are then averaged as the final association score of the MDP. Highlights of the BLNIMDA lie in the definition of MDA types, and the introduction of affinity weights evaluation from the bidirectional information distribution strategy and defined association types, which ensure the comprehensiveness and accuracy of the final prediction score of MDAs. Results Five-fold cross-validation and leave-one-out cross-validation are used to evaluate the performance of the BLNIMDA. The results of the Area Under Curve show that the BLNIMDA has many advantages over the other seven selected computational methods. Furthermore, the case studies based on four common diseases and miRNAs prove that the BLNIMDA has good predictive performance. Conclusions Therefore, the BLNIMDA is an effective method for predicting hidden MDAs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08908-8.

Project description:BackgroundSeveral types of RNA in the cell are usually involved in biological processes with multiple functions. Coding RNAs code for proteins while non-coding RNAs regulate gene expression. Some single-strand RNAs can create a circular shape via the back splicing process and convert into a new type called circular RNA (circRNA). circRNAs are among the essential non-coding RNAs in the cell that involve multiple disorders. One of the critical functions of circRNAs is to regulate the expression of other genes through sponging micro RNAs (miRNAs) in diseases. This mechanism, known as the competing endogenous RNA (ceRNA) hypothesis, and additional information obtained from biological datasets can be used by computational approaches to predict novel associations between disease and circRNAs.ResultsWe applied multiple classifiers to validate the extracted features from the heterogeneous network and selected the most appropriate one based on some evaluation criteria. Then, the XGBoost is utilized in our pipeline to generate a novel approach, called CircWalk, to predict CircRNA-Disease associations. Our results demonstrate that CircWalk has reasonable accuracy and AUC compared with other state-of-the-art algorithms. We also use CircWalk to predict novel circRNAs associated with lung, gastric, and colorectal cancers as a case study. The results show that our approach can accurately detect novel circRNAs related to these diseases.ConclusionsConsidering the ceRNA hypothesis, we integrate multiple resources to construct a heterogeneous network from circRNAs, mRNAs, miRNAs, and diseases. Next, the DeepWalk algorithm is applied to the network to extract feature vectors for circRNAs and diseases. The extracted features are used to learn a classifier and generate a model to predict novel CircRNA-Disease associations. Our approach uses the concept of the ceRNA hypothesis and the miRNA sponge effect of circRNAs to predict their associations with diseases. Our results show that this outlook could help identify CircRNA-Disease associations more accurately.

Dataset Information

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

Publications

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets