Looking for the Best Fit of a Function over Circadian Rhythm Data
Fecha
2019
Tipo
actas de congreso
Autores
Fallas Moya, Fabián
González Hernández, Manfred
Barboza Barquero, Luis Orlando
Obando Rodriguez, Kenneth
Valerio Cubillo, Ovidio
Holst Sanjuán, Andrea
Arias Madriz, Ronald Andrés
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Resumen
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.
Descripción
Palabras clave
circadian rhythm, regression, function fit, parameter optimization