Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis

A number of processes and pathways have been reported in the development of Group I pulmonary hypertension (Group I PAH); however, novel biomarkers need to be identified for a better diagnosis and management. We employed a robust rank aggregation (RRA) algorithm to shortlist the key differentially e...

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Bibliographic Details
Main Authors: Bajpai, A.K (Author), Cai, J. (Author), Hu, H. (Author), Huang, X. (Author), Li, B. (Author), Liu, J. (Author), Lu, L. (Author), Qi, D. (Author), Wang, C. (Author), Yu, L. (Author), Zhang, X. (Author), Zheng, F. (Author)
Format: Article
Language:English
Published: MDPI 2023
Subjects:
algorithm
Algorithms
bioinformatics
biological marker
biomarker
Biomarkers
Computational Biology
ferroptosis
genetics
Group I pulmonary hypertension
human
Humans
Hypertension, Pulmonary
immune infiltration
machine learning
Machine Learning
pathway enrichment analyses
protein–protein interaction
pulmonary hypertension
Online Access:View Fulltext in Publisher
View in Scopus

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