scholarly journals A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0163353 ◽  
Author(s):  
Mary Qu Yang ◽  
Laura Elnitski
Keyword(s):  
Systems Biology ◽  
Protein Interaction ◽  
Driver Mutations ◽  
Interaction Models ◽  
Protein Protein Interaction ◽  
Ovarian Cancers

scholarly journals Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Systems Biology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractMyocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

scholarly journals CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 145 ◽  
Author(s):  
Lilit Nersisyan ◽  
Ruben Samsonyan ◽  
Arsen Arakelyan
Keyword(s):  
Systems Biology ◽  
Protein Interaction ◽  
Kegg Pathway ◽  
Computer Assisted ◽  
Pathway Database ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Kegg Pathway Database ◽  
Context Specific ◽  
Biological Context

The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app (http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.

scholarly journals Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Systems Biology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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