BASiNETEntropy (version 0.99.6)

classify: 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
)

Value

Results

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.

Author

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)