Looking for the Best Fit of a Function over Circadian Rhythm Data

Fabian Fallas-Moya, Manfred Gonzalez-Hernandez, L. Barboza-Barquero, Kenneth Obando, Ovidio Valerio, Andrea Holst, Ronald Arias
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引用次数: 1

Abstract

Circadian rhythm regulates many biological processes. In plants, it controls the expression of genes related to growth and development. Recently, the usage of digital image analysis allows monitoring the circadian rhythm in plants, since the circadian rhythm can be observed by the movement of the leaves of a plant during the day. This is important because it can be used as a growth marker to select plants in plant breeding processes and to conduct fundamental science on this topic. In this work, a new algorithm is proposed to classify sets of coordinates to indicate if they show a circadian rhythm movement. Most algorithms take a set of coordinates and produce plots of the circadian movement, however, some databases have sets of coordinates that must be classified before the movement plots. This research presents an algorithm that determines if a set corresponds to a circadian rhythm movement using statistical analysis of polynomial regressions. Results showed that the proposed algorithm is significantly better compared with a Lagrange interpolation and with a fixed degree approaches. The obtained results suggest that using statistical information from the polynomial regressions can improve results in a classification task of circadian rhythm data.
在昼夜节律数据上寻找函数的最佳拟合
昼夜节律调节着许多生物过程。在植物中,它控制着与生长发育有关的基因的表达。最近,数字图像分析的使用允许监测植物的昼夜节律,因为昼夜节律可以通过植物叶片在白天的运动来观察。这一点很重要,因为它可以作为植物育种过程中选择植物的生长标记,并对这一主题进行基础科学研究。在这项工作中,提出了一种新的算法来对坐标集进行分类,以指示它们是否显示昼夜节律运动。大多数算法采用一组坐标并生成昼夜节律运动图,然而,一些数据库必须在运动图之前对一组坐标进行分类。本研究提出了一种算法,该算法使用多项式回归的统计分析来确定一组是否对应于昼夜节律运动。结果表明,该算法与拉格朗日插值法和固定度插值法相比,具有明显的优越性。所得结果表明,利用多项式回归的统计信息可以改善昼夜节律数据分类任务的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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