  This work presents a new approach for classification of genomic sequences
from measurements of complex networks and information theory. For this, it is
considered the nucleotides, dinucleotides and trinucleotides of a genomic
sequence. For each of them, the entropy, sum entropy and maximum entropy values
are calculated.For each of them is also generated a network, in which the nodes
are the nucleotides, dinucleotides or trinucleotides and its edges are
estimated by observing the respective adjacency among them in the genomic
sequence. In this way, it is generated three networks, for which measures of
complex networks are extracted.These measures together with measures of
information theory comprise a feature vector representing a genomic sequence.
Thus, the feature vector is used for classification by methods such as SVM,
MultiLayer Perceptron, J48, IBK, Naive Bayes and Random Forest in order to
evaluate the proposed approach.It was adopted coding sequences, intergenic
sequences and TSS (Transcriptional Starter Sites) as datasets, for which the
better results were obtained by the Random Forest with 91.2%, followed by J48
with 89.1% and SVM with 84.8% of accuracy. These results indicate that the new
approach of feature extraction has its value, reaching good levels of
classification even considering only the genomic sequences, i.e., no other a
priori knowledge about them is considered.
