Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies - Li, Ming; Chen, Fuli; Zhang, Yaling; Xiong, Yan; Li, Qiyong; Huang, Hui (Frontiers in Physiology, 2020)
Myocardial infarction (MI) is one type of serious heart attack in which the blood flow to heart is suddenly interrupted resulting in injuring heart muscles by lacking of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of myocardial infarction, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of the 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 approach 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. One hundred and thirty-four were determined to serve as features for building optimal SVM classifier 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.