classify function

Performs the classification methodology using complex network and entropy theories

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.

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.

Returns

Results

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)

Author(s)

Murilo Montanini Breve