An Evolutionary Trend Discovery Algorithm Based on Cubic Spline Interpolation
نویسندگان
چکیده
The speed of evolution, measured by the number of mutations over a fixed number of years, varies greatly in various branches of the evolutionary tree. This paper proposes an evolutionary trend discovery algorithm that reveals the distinguishing characteristics of any branch of the evolutionary tree. The evolutionary trend discovery algorithm is designed to work with either fossil-based data or automatically generated data about the age of the internal nodes in the evolutionary tree. The evolutionary trend discovery algorithm estimates the missing age data using cubic spline interpolation. The evolutionary trend discovery algorithm identifies, for example, that human evolution seems to be currently speeding up while the evolution of chickens is slowing down. Key-Words: Common mutations similarity matrix (CMSM), cubic splice interpolation, evolutionary tree, neighbor joining, phylogenetics, UPGMA
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