Scored Protein-Protein Interaction to Predict Subcellular Localizations for Yeast Using Diffusion Kernel

Abstract

Background: Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

“…The advancement in research and technology has been slowly shifting the focus of lung cancer diagnosis, prognosis and treatment towards understanding the underlying cause of disease progression using protein-protein interaction (PPI) networks, gene co-expression networks and molecular pathways. Though the PPI and co-expression networks are static in nature, these come with rich information about the dynamic processes such as behavior of genetic networks in response to DNA damage [6], prediction of protein subcellular localization [7][8][9][10][11][12], protein function [13], genetic interaction [14], process of aging [15], and protein network biomarkers [16][17][18][19][20][21][22]. The networks are of special interest because the genes do not act alone.…”

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

“…Ideker and Sharan [1] enumerated four different applications of protein networks to diseases: i) identifying new disease genes, ii) studying the network properties of disease genes, iii) classifying diseases based on protein network, and iv) identifying disease-related subnetworks. Genome-wide PPI networks come with rich information about the dynamic processes such as the behavior of genetic networks in response to DNA damage [2] and exposure to arsenic [3], the prediction of protein function [4], genetic interaction [5], protein subcellular localization [6][7][8][9][10][11], the process of aging [12], and protein network biomarkers [13][14][15].…”