%0 journal article
%@ 2352-3964
%A Wolfien, M., Klatt, D., Salybekov, A.A., Ii, M., Komatsu-Horii, M., Gaebel, R., Philippou-Massier, J., Schrinner, E., Akimaru, H., Akimaru, E., David, R., Garbade, J., Gummert, J., Haverich, A., Hennig, H., Iwasaki, H., Kaminski, A., Kawamoto, A., Klopsch, C., Kowallick, J.T., Krebs, S., Nesteruk, J., Reichenspurner, H., Ritter, C., Stamm, C., Tani-Yokoyama, A., Blum, H., Wolkenhauer, O., Schambach, A., Asahara, T., Steinhoff, G.

%D 2020
%J EBioMedicine

%P 102862 
%R doi:10.1016/j.ebiom.2020.102862
%T Hematopoietic stem-cell senescence and myocardial repair - Coronary artery disease genotype/phenotype analysis of post-MI myocardial regeneration response induced by CABG/CD133+ bone marrow hematopoietic stem cell treatment in RCT PERFECT Phase 3
%U https://doi.org/10.1016/j.ebiom.2020.102862
 
%X Myocardial repair is affected by HSC gene response and somatic mutation. Machine Learning can be utilized to identify and predict pathological HSC response.