Table_1_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX

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.

心肌梗死（Myocardial infarction，MI）是一类严重的心脏急症，当心脏的血液供应突然中断时，会因氧气供给不足引发心肌损伤。尽管临床诊断方法可用于识别心肌梗死的发生，但利用血液中分子标志物或特征分子的变化来表征心肌梗死的早期阶段与后续病程趋势，将有助于制定更为合理的治疗方案。既往的比较转录组学研究多聚焦于筛选心肌梗死患者与健康人群之间的差异表达基因，但不同病程阶段中发生改变的特征分子尚未得到充分挖掘。本研究开发了一套整合多种机器学习算法的计算方法，包括蒙特卡洛特征选择（Monte Carlo feature selection，MCFS）、增量特征选择（Incremental feature selection，IFS）与支持向量机（Support Vector Machine，SVM），用于识别心肌梗死不同病程阶段的基因表达特征。最终确定134个基因作为特征，用于构建区分急性心肌梗死与心肌梗死后阶段的最优支持向量机分类器。随后，对这134个基因开展功能富集分析与蛋白质相互作用分析，筛选出多个与心肌梗死进展相关的核心基因（IL1R1、TLR2及TLR4），这些基因可作为心肌梗死的新型诊断分子。