scholarly journals Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hua Yu ◽  
Lu Lu ◽  
Ming Chen ◽  
Chen Li ◽  
Jin Zhang
Keyword(s):  
State Of The Art ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
Disease Association ◽  
Computational Tool ◽  
Gene Interaction Network ◽  
Disease Pathogenesis ◽  
Link Type ◽  
Genome Wide

AbstractMany of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer’s disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet.

scholarly journals Genome-wide discovery of hidden genes mediating known drug-disease association

2019 ◽  
Author(s):  
Hua Yu ◽  
Lu Lu ◽  
Ming Chen ◽  
Chen Li ◽  
Jin Zhang
Keyword(s):  
Interaction Network ◽  
Drug Repurposing ◽  
Minimum Cost ◽  
Disease Association ◽  
Computational Tool ◽  
Minimum Cost Flow ◽  
Functional Interaction ◽  
Disease Pathogenesis ◽  
Link Type ◽  
Genome Wide

AbstractIdentifying of hidden genes mediating Known Drug-Disease Association (KDDA) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we present a novel computational tool, called KDDANet, for systematic and accurate uncovering hidden genes mediating KDDA from the perspective of genome-wide gene functional interaction network. By implementing minimum cost flow optimization, combined with depth first searching and graph clustering on a unified flow network model, KDDANet outperforms existing methods in both sensitivity and specificity of identifying genes in mediating KDDA. Case studies on Alzheimer’s disease (AD) and obesity uncover the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulates known genes mediating KDDAs related to cancer, but also uncovers novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet/.

scholarly journals Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2117
Author(s):  
Vlad Groza ◽  
Mihai Udrescu ◽  
Alexandru Bozdog ◽  
Lucreţia Udrescu
Keyword(s):  
Interaction Network ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
New Drugs ◽  
Interaction Data ◽  
Gene Interaction Network ◽  
Drug Databases ◽  
Drug Similarity

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

scholarly journals Prognostic Genetic Markers for Thrombosis in COVID-19 Patients: A Focused Analysis on D-Dimer, Homocysteine and Thromboembolism

2020 ◽  
Vol 11 ◽  
Author(s):  
Mohamed Abu-Farha ◽  
Salman Al-Sabah ◽  
Maha M. Hammad ◽  
Prashantha Hebbar ◽  
Arshad Mohamed Channanath ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
Cerebrovascular Disorders ◽  
Vascular Diseases ◽  
Interaction Network ◽  
Gene Interaction ◽  
Specific Gene ◽  
Nucleotide Polymorphisms ◽  
Gene Interaction Network ◽  
Genome Wide ◽  
D Dimer

COVID-19 is caused by Severe Acute Respiratory Syndrome Coronavirus-2, which has infected over thirty eight million individuals worldwide. Emerging evidence indicates that COVID-19 patients are at a high risk of developing coagulopathy and thrombosis, conditions that elevate levels of D-dimer. It is believed that homocysteine, an amino acid that plays a crucial role in coagulation, may also contribute to these conditions. At present, multiple genes are implicated in the development of these disorders. For example, single-nucleotide polymorphisms (SNPs) in FGG, FGA, and F5 mediate increases in D-dimer and SNPs in ABO, CBS, CPS1 and MTHFR mediate differences in homocysteine levels, and SNPs in TDAG8 associate with Heparin-induced Thrombocytopenia. In this study, we aimed to uncover the genetic basis of the above conditions by examining genome-wide associations and tissue-specific gene expression to build a molecular network. Based on gene ontology, we annotated various SNPs with five ancestral terms: pulmonary embolism, venous thromboembolism, vascular diseases, cerebrovascular disorders, and stroke. The gene-gene interaction network revealed three clusters that each contained hallmark genes for D-dimer/fibrinogen levels, homocysteine levels, and arterial/venous thromboembolism with F2 and F5 acting as connecting nodes. We propose that genotyping COVID-19 patients for SNPs examined in this study will help identify those at greatest risk of complications linked to thrombosis.

scholarly journals Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Fei Shen ◽  
Wensong Cai ◽  
Xiaoxiong Gan ◽  
Jianhua Feng ◽  
Zhen Chen ◽  
...  
Keyword(s):  
Genetic Factors ◽  
Characteristic Curve ◽  
Interaction Network ◽  
Gene Interaction ◽  
Point Of View ◽  
Gene Interaction Network ◽  
Novel Genes ◽  
Potential Health ◽  
Statistical Point ◽  
Genome Wide

The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.

scholarly journals Genome-Wide Identification, Evolution, and Expression Analysis of TPS and TPP Gene Families in Brachypodium distachyon

Plants ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 362 ◽  
Author(s):  
Song Wang ◽  
Kai Ouyang ◽  
Kai Wang
Keyword(s):  
Stress Responses ◽  
Brachypodium Distachyon ◽  
Interaction Network ◽  
Purifying Selection ◽  
Gene Interaction ◽  
Gene Families ◽  
Gene Interaction Network ◽  
Genome Wide ◽  
Duplication Events ◽  
Gene Structures

Trehalose biosynthesis enzyme homologues in plants contain two families, trehalose-6-phosphate synthases (TPSs) and trehalose-6-phosphate phosphatases (TPPs). Both families participate in trehalose synthesis and a variety of stress-resistance processes. Here, nine BdTPS and ten BdTPP genes were identified based on the Brachypodium distachyon genome, and all genes were classified into three classes. The Class I and Class II members differed substantially in gene structures, conserved motifs, and protein sequence identities, implying varied gene functions. Gene duplication analysis showed that one BdTPS gene pair and four BdTPP gene pairs are formed by duplication events. The value of Ka/Ks (non-synonymous/synonymous) was less than 1, suggesting purifying selection in these gene families. The cis-elements and gene interaction network prediction showed that many family members may be involved in stress responses. The quantitative real-time reverse transcription (qRT-PCR) results further supported that most BdTPSs responded to at least one stress or abscisic acid (ABA) treatment, whereas over half of BdTPPs were downregulated after stress treatment, implying that BdTPSs play a more important role in stress responses than BdTPPs. This work provides a foundation for the genome-wide identification of the B. distachyon TPS–TPP gene families and a frame for further studies of these gene families in abiotic stress responses.

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
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