CYCLOPS:周期性结构的周期性排序(CYCLOPS),用于研究人体组织中的昼夜节律-源码

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Cambridge UROP 2020:CYCLOPS在识别生物数据节律中的应用 作者:亨利·林(Henry Lim) 背景 昼夜节律影响生理和行为的许多方面,并调节哺乳动物的许多过程,包括体温,血压和运动能力。 由于现有的大规模数据集很少包含一天中的时间,因此识别人类分子机制具有挑战性。 为了解决这个问题,我们结合了对周期性结构,进化保护和无监督机器学习的理解,以沿着周期性周期对无序的人体活检数据进行排序。 该项目解决了从此类数据推断时间标签以识别人类和其他哺乳动物基因的昼夜节律的问题。 在本项目中研究的算法(按周期性结构的循环排序(CYCLOPS))利用进化守恒和机器学习来识别高维数据中的椭圆结构。 通过这种结构,CYCLOPS估计每个样本的相位。 我们首先使用人工生成的振荡数据,再使用按时间排序的鼠标和人类数据,对CYCLOPS进行了验证,并证明了其一致性。 介绍 CYCLOPS的

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