DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

2020 ◽  
Vol 36 (16) ◽  
pp. 4466-4472 ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Jiajie Peng ◽  
Liang Cheng
Keyword(s):  
Target Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Biological Data ◽  
Supplementary Information ◽  
Gene Interaction Network ◽  
Convolutional Network ◽  
Protein Coding ◽  
Lncrna Gene ◽  
Novel Target

Abstract Motivation Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. Results In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA–gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90–0.98) and area under precision-recall curves (0.91–0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. Availability and implementation https://github.com/zty2009/LncRNA-target-gene. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Potential Mechanisms of Triptolide against Diabetic Cardiomyopathy Based on Network Pharmacology Analysis and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ning Zhu ◽  
Bingwu Huang ◽  
Liuyan Zhu ◽  
Yi Wang
Keyword(s):  
Signaling Pathways ◽  
Diabetic Cardiomyopathy ◽  
Target Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Network Pharmacology ◽  
Gene Interaction Network ◽  
Protein Targets ◽  
Pathway Network ◽  
Underlying Mechanisms

The incidence of heart failure was significantly increased in patients with diabetic cardiomyopathy (DCM). The therapeutic effect of triptolide on DCM has been reported, but the underlying mechanisms remain to be elucidated. This study is aimed at investigating the potential targets of triptolide as a therapeutic strategy for DCM using a network pharmacology approach. Triptolide and its targets were identified by the Traditional Chinese Medicine Systems Pharmacology database. DCM-associated protein targets were identified using the comparative toxicogenomics database and the GeneCards database. The networks of triptolide-target genes and DCM-associated target genes were created by Cytoscape. The common targets and enriched pathways were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The gene-gene interaction network was analyzed by the GeneMANIA database. The drug-target-pathway network was constructed by Cytoscape. Six candidate protein targets were identified in both triptolide target network and DCM-associated network: STAT3, VEGFA, FOS, TNF, TP53, and TGFB1. The gene-gene interaction based on the targets of triptolide in DCM revealed the interaction of these targets. Additionally, five key targets that were linked to more than three genes were determined as crucial genes. The GO analysis identified 10 biological processes, 2 cellular components, and 10 molecular functions. The KEGG analysis identified 10 signaling pathways. The docking analysis showed that triptolide fits in the binding pockets of all six candidate targets. In conclusion, the present study explored the potential targets and signaling pathways of triptolide as a treatment for DCM. These results illustrate the mechanism of action of triptolide as an anti-DCM agent and contribute to a better understanding of triptolide as a transcriptional regulator of cytokine mRNA expression.

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

2020 ◽  
Author(s):  
Wei Wang ◽  
Wei Liu
Keyword(s):  
Cox Model ◽  
Gene Interaction ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Gene Interaction Network ◽  
Clinical Cancer Research ◽  
Prognostic Performance ◽  
Interaction Information ◽  
Cancer Types ◽  
Risk Of Cancer

Abstract Motivation Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets. Results Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

10.1186/gm404 ◽  
2012 ◽  
Vol 4 (12) ◽  
Author(s):  
Raymond J Louie ◽  
Jingyu Guo ◽  
John W Rodgers ◽  
Rick White ◽  
Najaf A Shah ◽  
...  
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network ◽  
Protein Biogenesis

Rheumatoid arthritis–associated DNA methylation sites in peripheral blood mononuclear cells

2018 ◽  
Vol 78 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Hong Zhu ◽  
Long-Fei Wu ◽  
Xing-Bo Mo ◽  
Xin Lu ◽  
Hui Tang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mrna Expression ◽  
Mononuclear Cells ◽  
Interaction Network ◽  
Gene Interaction ◽  
Jurkat Cells ◽  
Gene Interaction Network ◽  
Peripheral Blood Mononuclear ◽  
Inference Tests ◽  
Inducible Gene

ObjectivesTo identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism.MethodsWe performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells.ResultsA total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation–mRNA–RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1, IFI44L, DTX3L and PARP9). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2).ConclusionsThis multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA.

scholarly journals Retraction Note: Gene-gene interaction network analysis of ovarian cancer using TCGA data

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Huanchun Ying ◽  
Jing Lv ◽  
Tianshu Ying ◽  
Shanshan Jin ◽  
Jingru Shao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network

Autism candidate genes in cytogenetic regions of interest identified with a gene interaction network

2006 ◽  
Vol 16 ◽  
pp. S6
Author(s):  
W. Staal ◽  
L. Franke ◽  
J.A.S. Vorstman ◽  
H. van Engeland ◽  
C. Wijminga
Keyword(s):  
Candidate Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Regions Of Interest ◽  
Gene Interaction Network
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