AEMDA: inferring miRNA–disease associations based on deep autoencoder

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.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
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.

scholarly journals GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

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.

scholarly journals EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments

2020 ◽  
Vol 49 (D1) ◽  
pp. D86-D91
Author(s):  
Bailing Zhou ◽  
Baohua Ji ◽  
Kui Liu ◽  
Guodong Hu ◽  
Fei Wang ◽  
...  
Keyword(s):  
Interaction Network ◽  
Circular Rnas ◽  
Biological Processes ◽  
Drug Resistant ◽  
Functional Roles ◽  
Visual Interaction ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Central Database ◽  
Data Browsing

Abstract Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

scholarly journals Regulatory effects of lncRNAs and miRNAs on autophagy in malignant tumorigenesis

2018 ◽  
Vol 38 (5) ◽  
Author(s):  
Qingqing Yin ◽  
Wei Feng ◽  
Xianjuan Shen ◽  
Shaoqing Ju
Keyword(s):  
Evolutionary Conservation ◽  
Disease Diagnosis ◽  
Biological Processes ◽  
Important Process ◽  
Biological Phenomenon ◽  
Substrate Degradation ◽  
Endogenous Substrate ◽  
Non Coding Rnas ◽  
Regulatory Effects ◽  
Underlying Mechanisms

Autophagy is an important process in endogenous substrate degradation by lysosomes within cells, with a degree of evolutionary conservation. Like apoptosis and cell senescence, cell autophagy is a very important biological phenomenon involving the development and growth of biological processes. Abnormal autophagy may lead to tumorigenesis. In recent years, increasing studies have demonstrated that long non-coding RNAs (lncRNAs) and miRNAs can regulate cell autophagy by modulating targetting gene expression. In this review, we will provide an overview of lncRNAs and miRNAs in autophagy modulation and new insights into the underlying mechanisms, as well as their potential utilization in disease diagnosis, prognosis, and therapy.

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

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
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.

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

2018 ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Qianqian Li ◽  
Bolin Chen ◽  
Qinghua Jiang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Essential Feature ◽  
Source Code ◽  
Disease Association ◽  
Feature Representation ◽  
Supplementary Information ◽  
Feature Combination ◽  
Biological Processes ◽  
Non Coding Rna ◽  
Supplementary Material

AbstractMotivationA 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.ResultsWe propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. 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.AvailabilityThe source code and data are available at https://github.com/Issingjessica/[email protected];[email protected];[email protected] informationSupplementary data are available at Bioinformatics online.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yang Liu ◽  
Xueyong Li ◽  
Xiang Feng ◽  
Lei Wang
Keyword(s):  
Computational Model ◽  
Nearest Neighbor ◽  
Esophageal Neoplasms ◽  
State Of The Art ◽  
Biological Processes ◽  
Recommendation Algorithm ◽  
Diagnosis And Prognosis ◽  
Disease Associations ◽  
Independent Case ◽  
Previous State

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.

Predicting miRNA-disease interaction based on recommend method

2019 ◽  
Vol 48 (1) ◽  
pp. 35-40
Author(s):  
Qingfeng Chen ◽  
Zhe Zhao ◽  
Wei Lan ◽  
Ruchang Zhang ◽  
Jiahai Liang
Keyword(s):  
Disease Diagnosis ◽  
Computational Method ◽  
Computational Framework ◽  
Content Type ◽  
Interaction Prediction ◽  
Disease Occurrence ◽  
Non Coding Rnas ◽  
Function Similarity ◽  
Traditional Approaches ◽  
The Relationship

Purpose MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions. Design/methodology/approach A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method. Findings To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods. Originality/value This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.

Structural Alignment of RNAs with Pseudoknots

2011 ◽  
pp. 550-571
Author(s):  
Thomas K. F. Wong ◽  
S. M. Yiu
Keyword(s):  
Secondary Structure ◽  
De Novo ◽  
State Of The Art ◽  
Structural Alignment ◽  
Structural Similarity ◽  
Secondary Structures ◽  
Biological Processes ◽  
Computational Approaches ◽  
Different Types ◽  
Non Coding Rnas

Non-coding RNAs (ncRNAs) are found to be critical for many biological processes. However, identifying these molecules is very difficult and challenging due to the lack of strong detectable signals such as opening read frames. Most computational approaches rely on the observation that the secondary structures of ncRNA molecules are conserved within the same family. Aligning a known ncRNA to a target candidate to determine the sequence and structural similarity helps in identifying de novo ncRNA molecules that are in the same family of the known ncRNA. However, the problem becomes more difficult if the secondary structure contains pseudoknots. Only until recently, many of the existing approaches could not handle structures with pseudoknots. This chapter reviews the state-of-the-art algorithms for different types of structures that contain pseudoknots including standard pseudoknot, simple non-standard pseudoknot, recursive standard pseudoknot, and recursive simple non-standard pseudoknot. Although none of the algorithms is designed for general pseudoknots, these algorithms already cover all known ncRNAs in both Rfam and PseudoBase databases. The evaluation of the algorithms also shows that the approach is useful in identifying ncRNA molecules in other species, which are in the same family of a known ncRNA.

Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer

2019 ◽  
Vol 35 (21) ◽  
pp. 4344-4349 ◽  
Author(s):  
Yuwei Zhang ◽  
Yang Tao ◽  
Huihui Ji ◽  
Wei Li ◽  
Xingli Guo ◽  
...  
Keyword(s):  
Network Analysis ◽  
Regulatory Network ◽  
Essential Genes ◽  
Supplementary Information ◽  
Published Data ◽  
Biological Processes ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rnas ◽  
Pan Cancer

Abstract Motivation Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. Methods CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. Results We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a ‘bi-colored’ network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. Availability and implementation   Supplementary information Supplementary data are available at Bioinformatics online.

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