Abstract
Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.
Highlights
Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays
The results indicate that the protein tissue factor pathway inhibitor is a strong predictor for describing the pathological differences between the acute and the stable phase of STEMI
Using a random forest model, the protein biomarkers tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to have high importance for describing the pathological differences between the acute phase and stable phase for patients with STEMI. These five proteins are good candidates for further studies investigating biomarkers for healing processes in STEMI patients, and can contribute to an in-depth understanding of the molecular processes occurring from primary percutaneous coronary intervention (PPCI) to three months follow-up
Summary
Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. The advent of large-scale complex data has, made it difficult to use conventional approaches for data analysis; the use of advanced statistical and machine learning methods is highly relevant to obtain in-depth scientific knowledge and discover novel biomarkers. The first approach uses qualitative and more conventional methods by reviewing published literature to investigate if a specific protein has previously been identified as a candidate biomarker or associated with a specific disease or pathological state This approach quickly becomes infeasible if the investigated data set is large and complex. Kulasingam et al presented a qualitative analysis of the data focusing on identification of significant differential expression of the multiplex protein data between the acute and the stable phase
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