  The discovery of nucleic acids and the structure of DNA have brought
considerable advances in the understanding of life. The development of
next-generation sequencing technologies has led to a large-scale generation of
data, for which computational methods have become essential for analysis and
knowledge discovery. In particular, RNAs have received much attention because
of the diversity of their functionalities in the organism and the discoveries
of different classes with different functions in many biological processes.
Therefore, the correct identification of RNA sequences is increasingly
important to provide relevant information to understand the functioning of
organisms. This work addresses this context by presenting a new method for the
classification of biological sequences through complex networks and entropy
maximization. The maximum entropy principle is proposed to identify the most
informative edges about the RNA class, generating a filtered complex network.
The proposed method was evaluated in the classification of different RNA
classes from 13 species. The proposed method was compared to PLEK, CPC2 and
BASiNET methods, outperforming all compared methods. BASiNETEntropy classified
all RNA sequences with high accuracy and low standard deviation in results,
showing assertiveness and robustness. The proposed method is implemented in an
open source in R language and is freely available at
https://cran.r-project.org/web/packages/BASiNETEntropy.
