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The discovery of nucleic acids and the structure of DNA have broughtconsiderable advances in the understanding of life. The development ofnext-generation sequencing technologies has led to a large-scale generation ofdata, for which computational methods have become essential for analysis andknowledge discovery. In particular, RNAs have received much attention becauseof the diversity of their functionalities in the organism and the discoveriesof different classes with different functions in many biological processes.Therefore, the correct identification of RNA sequences is increasinglyimportant to provide relevant information to understand the functioning oforganisms. This work addresses this context by presenting a new method for theclassification of biological sequences through complex networks and entropymaximization. The maximum entropy principle is proposed to identify the mostinformative edges about the RNA class, generating a filtered complex network.The proposed method was evaluated in the classification of different RNAclasses from 13 species. The proposed method was compared to PLEK, CPC2 andBASiNET methods, outperforming all compared methods. BASiNETEntropy classifiedall RNA sequences with high accuracy and low standard deviation in results,showing assertiveness and robustness. The proposed method is implemented in anopen source in R language and is freely available athttps://cran.r-project.org/web/packages/BASiNETEntropy.

BASiNETEntropy: an alignment-free method for classification of biological sequences through complex networks and entropy maximization