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Using Machine Learning to Elucidate the Spatial and Genetic Complexity of the Ascending Aorta
medRxiv - Cardiovascular Medicine Pub Date : 2021-11-02 , DOI: 10.1101/2021.11.01.21265701 Mahan Nekoui , James Pirruccello , Paolo Di Achille , Seung Hoan Choi , Samuel Friedman , Victor Nauffal , Kenney Ng , Puneet Batra , Jennifer Ho , Anthony Philippakis , Steven Lubitz , Mark Lindsay , Patrick Ellinor
medRxiv - Cardiovascular Medicine Pub Date : 2021-11-02 , DOI: 10.1101/2021.11.01.21265701 Mahan Nekoui , James Pirruccello , Paolo Di Achille , Seung Hoan Choi , Samuel Friedman , Victor Nauffal , Kenney Ng , Puneet Batra , Jennifer Ho , Anthony Philippakis , Steven Lubitz , Mark Lindsay , Patrick Ellinor
Background The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined genetics of thoracic aortic diameter in a single plane. We sought to elucidate the genetic basis for the diameter of the LVOT, the aortic root, and the ascending aorta.
中文翻译:
使用机器学习阐明升主动脉的空间和遗传复杂性
背景左心室流出道 (LVOT) 和升主动脉空间复杂,具有不同的病理学和胚胎学起源。先前的工作检查了单个平面中胸主动脉直径的遗传学。我们试图阐明 LVOT、主动脉根部和升主动脉直径的遗传基础。
更新日期:2021-11-05
中文翻译:
使用机器学习阐明升主动脉的空间和遗传复杂性
背景左心室流出道 (LVOT) 和升主动脉空间复杂,具有不同的病理学和胚胎学起源。先前的工作检查了单个平面中胸主动脉直径的遗传学。我们试图阐明 LVOT、主动脉根部和升主动脉直径的遗传基础。