Key result
A machine learning model utilizing 134 selected gene features successfully distinguished between acute, 30-day post, and 1-year post myocardial infarction phases with a cross-validation accuracy of 0.831.
Why the study?
Signature molecules altered across different phases of myocardial infarction have not been well excavated to help characterize early phase and later trends for treatment planning.
Can machine learning-based feature selection identify specific blood gene expression signatures to distinguish different phases of myocardial infarction?
Can machine learning-based feature selection identify specific blood gene expression signatures to distinguish different phases of myocardial infarction?
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Machine learning-based feature selection identified a 134-gene signature, including hub genes IL1R1, TLR2, and TLR4, capable of distinguishing acute from post-myocardial infarction phases, offering potential novel diagnostic biomarkers.
Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.
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Li et al. (2020) studied Myocardial Infarction (n=166). 134-gene expression signature vs. Different phases of MI (D0, D30, Y1) was evaluated on Leave-one-out cross-validation (LOOCV) accuracy of the SVM classifier. A machine learning model utilizing 134 selected gene features successfully distinguished between acute, 30-day post, and 1-year post myocardial infarction phases with a cross-validation accuracy of 0.831.
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