scholarly journals A three-gene signature from protein-protein interaction network of LOXL2 - and actin-related proteins for esophageal squamous cell carcinoma prognosis

2017 ◽  
Vol 6 (7) ◽  
pp. 1707-1719 ◽  
Author(s):  
Xiu-hui Zhan ◽  
Ji-wei Jiao ◽  
Hai-feng Zhang ◽  
Chun-quan Li ◽  
Jian-mei Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Gene Signature ◽  
Protein Protein Interaction ◽  
Related Proteins ◽  
Protein Protein Interaction Network

A network-based approach reveals novel invasion and Maurer's clefts-related proteins in Plasmodium falciparum

2019 ◽  
Vol 15 (6) ◽  
pp. 431-441 ◽  
Author(s):  
Dibyajyoti Das ◽  
Sowmya Ramaswamy Krishnan ◽  
Arijit Roy ◽  
Gopalakrishnan Bulusu
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Disease Pathogenesis ◽  
Protein Protein Interaction ◽  
Novel Proteins ◽  
Maurer's Clefts ◽  
Related Proteins ◽  
Protein Protein Interaction Network

To understand disease pathogenesis, all the disease-related proteins must be identified. In this work, known proteins were used to identify related novel proteins using RWR method on a dynamic P. falciparum protein–protein interaction network.

Protein–protein interaction network with machine learning models and multiomics data reveal potential neurodegenerative disease-related proteins

2020 ◽  
Vol 29 (8) ◽  
pp. 1378-1387 ◽  
Author(s):  
Xinjian Yu ◽  
Siqi Lai ◽  
Hongjun Chen ◽  
Ming Chen
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Computational Study ◽  
Interaction Network ◽  
Model Organisms ◽  
Protein Protein Interaction ◽  
Related Proteins ◽  
Protein Protein Interaction Network

Abstract Research of protein–protein interaction in several model organisms is accumulating since the development of high-throughput experimental technologies and computational methods. The protein–protein interaction network (PPIN) is able to examine biological processes in a systematic manner and has already been used to predict potential disease-related proteins or drug targets. Based on the topological characteristics of the PPIN, we investigated the application of the random forest classification algorithm to predict proteins that may cause neurodegenerative disease, a set of pathological changes featured by protein malfunction. By integrating multiomics data, we further showed the validity of our machine learning model and narrowed down the prediction results to several hub proteins that play essential roles in the PPIN. The novel insights into neurodegeneration pathogenesis brought by this computational study can indicate promising directions for future experimental research.

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