classification: Performs the classification methodology using complex network...

In EricIto/BASiNET: BASiNET - Classification of mRNA and lncRNA sequences using complex network theory

Description

Given two distinct data sets, one of mnRNA and one of lncRNA. The classification of the data is done from the structure of the networks formed by the sequences. After this is done classifying with the J48 classifier and randomForest. It is also created in the current directory a file of type arff called' result 'with all values so that it can be used later.

Usage

classification(mRNA, lncRNA, word = 3, step = 1, sncRNA, graphic, graphic3D,
  classifier = c("J48", "RF"))

Arguments

mRNA	Directory where the file .FASTA lies with the mRNA sequences
lncRNA	Directory where the file is located fasta with lncRNA sequences
word	Integer that defines the size of the word to parse. By default the word parameter is set to 3
step	Integer that determines the distance that will be traversed in the sequences for creating a new connection. By default the step parameter is set to 1
sncRNA	Directory where the file is located fasta with the sncRNA sequences (OPTIONAL)
graphic	Parameter of the logical type, TRUE or FALSE for graphics generation. As default graphic gets FALSE
graphic3D	Parameter of the logical type, TRUE or FALSE for 3D graphics generation. As default graphic3D gets FALSE
classifier	Character Parameter. By default the classifier is J48, but the user can choose to use randomForest by configuring as classifier = "RF"

Value

Data.frame with the results of measures

Author(s)

Eric Augusto Ito

Examples

arqSeqMRNA <- system.file("extdata", "sequences2.fasta", package = "BASiNET")
arqSeqLNCRNA <- system.file("extdata", "sequences.fasta", package = "BASiNET")
classification(mRNA=arqSeqMRNA,lncRNA=arqSeqLNCRNA,word=3,step=3,graphic=FALSE,graphic3D=FALSE)

EricIto/BASiNET documentation built on May 28, 2019, 12:38 p.m.