Results of machine learning based biomarker screening analysis.

Results of machine learning based biomarker screening analysis.

<p>(A) Feature selection graph of LASSO algorithm. (B) Error rate graph of SVM-RFE algorithm. The red dots indicate the numbers of candidate candidate genes with the minimal false discovery rate. (C) Sorting graph of gene importance scores for the RandomForest algorithm. (D) Venn diagram of th...

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Bibliographic Details
Main Author: Yatan Li (21439415) (author)
Other Authors: Wei Jia (136607) (author), Chen Chen (6544) (author), Cheng Chen (136164) (author), Jinchao Chen (2852384) (author), Xinling Yang (420177) (author), Pei Liu (93392) (author)
Published: 2025
Subjects:
Cell Biology
Immunology
Biological Sciences not elsewhere classified
random forest algorithms
potential therapeutic targets
possible regulatory nodes
parkinson &# 8217
experimental results showed
debilitating neurodegenerative disorder
drug sensitivity analysis
cell transcriptomic analysis
mirna network construction
biomarker genes related
screen biomarker genes
mirna network
biomarker genes
three biomarkers
signaling pathways
seventeen co
roc curves
ppi networks
new biomarkers
machine learning
inhibitory neurons
inflammatory response
excitatory neurons
cytokine activity
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