Network Biology, Sequence, Pathway and Ontology Informatics

2019 ◽  
pp. 175-187
Author(s):  
Ju Han Kim
Keyword(s):  
Network Biology

scholarly journals Network Biology Approach to Identify the Active Ingredients, Potential Targets, and Signaling Pathways in Phellinus Baumii and Salvia Miltiorrhiza Bunge on Vascular Health (P15-025-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Yeni Lim ◽  
Oran Kwon
Keyword(s):  
Clinical Trial ◽  
Signaling Pathways ◽  
Platelet Activation ◽  
Vascular Dysfunction ◽  
Salvia Miltiorrhiza ◽  
Network Biology ◽  
Vascular Health ◽  
Salvia Miltiorrhiza Bunge ◽  
Phellinus Baumii ◽  
Target Molecules

Abstract Objectives Increasing attention has been paid to a range of botanical food supplement that help to maintain vascular health. Multiple components in botanical foods are expected to be working in concert with various targets. In a previous our animal study, Phellinus baumii and Salvia miltiorrhiza Bunge (PS) ameliorated endothelial and vascular dysfunction in a platelet activation rat model. This study aimed to provide the components, target molecules, phenotypes, signaling pathways, and investigate the mechanism of PS on vascular health. Methods Network biology analysis was based on the data from two clinical trials. The first clinical trial was performed in healthy subjects using high-fat-induced vascular dysfunction model. The second clinical trial was performed in healthy smokers. Differential markers obtained from clinical data, Affymetrix microarray, metabolomics, together with ingredient of PS, were mapped onto the network platform termed the context-oriented directed associations. A network of “component-target-phenotype-pathway” was constructed. Results The resulting vascular health network demonstrates that the components of PS are linked various target molecules for adhesion molecule production, platelet activation, endothelial inflammation, vascular dilation, and mitochondrial metabolism and detoxification, implicated with various metabolic pathways. Conclusions Using network biology methods, this study revealed the components and their target molecules, phenotypes, signaling pathways and provided wider information to support the synergistic mechanisms of PS on vascular health. Funding Sources This research was funded by the Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science & ICT and the BK21PLUS of the National Research Foundation.

Big data, integrative omics and network biology

2021 ◽  
Author(s):  
Priya Tolani ◽  
Srishti Gupta ◽  
Kirti Yadav ◽  
Suruchi Aggarwal ◽  
Amit Kumar Yadav
Keyword(s):  
Big Data ◽  
Network Biology ◽  
Integrative Omics

scholarly journals A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

2016 ◽  
Vol 12 (4) ◽  
pp. e1004884 ◽  
Author(s):  
Victor Trevino ◽  
Alberto Cassese ◽  
Zsuzsanna Nagy ◽  
Xiaodong Zhuang ◽  
John Herbert ◽  
...  
Keyword(s):  
Cross Talk ◽  
Prostate Carcinoma ◽  
Network Biology ◽  
Carcinoma Cells ◽  
Normal Prostate

scholarly journals Deciphering the molecular basis of human cardiovascular disease through network biology

2012 ◽  
Vol 27 (3) ◽  
pp. 202-209 ◽  
Author(s):  
Stephen Y. Chan ◽  
Kevin White ◽  
Joseph Loscalzo
Keyword(s):  
Cardiovascular Disease ◽  
Molecular Basis ◽  
Network Biology

scholarly journals A Novel Malaria Pf/Pv Ab Rapid Diagnostic Test Using a Differential Diagnostic Marker Identified by Network Biology

2016 ◽  
Vol 12 (7) ◽  
pp. 824-835 ◽  
Author(s):  
Sung Jin Cho ◽  
Jihoo Lee ◽  
Hyun Jae Lee ◽  
Hyun-Young Jo ◽  
Mangalam Sinniah ◽  
...  
Keyword(s):  
Diagnostic Test ◽  
Rapid Diagnostic Test ◽  
Diagnostic Marker ◽  
Network Biology

scholarly journals Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 525 ◽  
Author(s):  
Samar Tareen ◽  
Michiel Adriaens ◽  
Ilja Arts ◽  
Theo de Kok ◽  
Roel Vink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Weight Loss ◽  
Adipose Tissue ◽  
Molecular Mechanisms ◽  
Dietary Intervention ◽  
Network Biology ◽  
Analysis Tool ◽  
Cellular Processes ◽  
Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

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