Paper Info
Title
Gene Expression-Based Prediction of Lung Cancer-Associated Protein-Protein Interactions.
Abstract
The most prevalent form of cancer in the world is lung cancer. Although many qualitative and quantitative studies screened the proteins related to lung cancer over the years, protein-protein interactions (PPIs) related to lung cancer are not identified until now. Therefore in this article, we have aimed to analyze specific PPIs in lung cancer tissues using a microarray gene expression dataset. To identify the genes that are up-regulated and down-regulated during lung cancer progression in the human body, firstly, the differentially expressed genes are extracted from the lung cancer microarray dataset. Then, the PPI network of these proteins is constructed using the STRING web server. After this, we have collected pairwise features, like, sequence similarity score, gene ontology(GO)-based semantic score, domain-domain similarity score and, average shortest path length (ASPL) of the interacting protein pairs. Finally, a total of 10042 PPIs from HPRD, related to lung cancer are predicted using the NNET algorithm. In addition, this projected human PPI network’s literature filtering and KEGG pathway of the hub proteins are studied and reported in the paper. Although many hub proteins related to lung cancer has been analyzed over the years, we have identified a new set of hub proteins of lung cancer in this study.
Year
DOI
Venue
2021
10.1007/978-3-030-75529-4_17
CICBA
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Lopamudra Dey161.85
Anirban Mukhopadhyay271150.07