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

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.