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

In BASiNETEntropy: Classification of RNA Sequences using Complex Network and Information Theory

Performs the classification methodology using complex network and entropy theories

Description

Given three or two distinct data sets, one of mRNA, one of lncRNA and one of sncRNA. The classification of the data is done from the structure of the networks formed by the sequences, that is filtered by an entropy methodology. After this is done, the classification starts.

Usage

classify(
  mRNA,
  lncRNA,
  sncRNA = NULL,
  trainingResult,
  save_dataframe = NULL,
  save_model = NULL,
  predict_with_model = NULL
)

Arguments

mRNA	Directory where the file .FASTA lies with the mRNA sequences
lncRNA	Directory where the file .FASTA lies with the lncRNA sequences
sncRNA	Directory where the file .FASTA lies with the sncRNA sequences (optional)
trainingResult	The result of the training, (three or two matrices)
save_dataframe	save when set, this parameter saves a .csv file with the features in the current directory. No file is created by default.
save_model	save when set, this parameter saves a .rds file with the model in the current directory. No file is created by default.
predict_with_model	predict the input sequences with the previously generated model.

Value

Results

Author(s)

Murilo Montanini Breve

Examples

library(BASiNETEntropy)
arqSeqMRNA <- system.file("extdata", "mRNA.fasta",package = "BASiNETEntropy")
arqSeqLNCRNA <- system.file("extdata", "ncRNA.fasta", package = "BASiNETEntropy")
load(system.file("extdata", "trainingResult.RData", package = "BASiNETEntropy"))
r_classify <- classify(mRNA=arqSeqMRNA, lncRNA=arqSeqLNCRNA, trainingResult = trainingResult)

BASiNETEntropy documentation built on Aug. 17, 2023, 1:06 a.m.