KNN_RNA: K-Nearest Neighbor_RNA (KNN_RNA)
In ftrCOOL: Feature Extraction from Biological Sequences

Description Usage Arguments Value References Examples

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

1	KNN_RNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

`seqs`	is a FASTA file containing ribonucleotide sequences. The sequences start with '>'. Also, seqs could be a string vector. Each element of the vector is a ribonucleotide sequence.
`trainSeq`	is a fasta file with ribonucleotide sequences. Each sequence starts with a '>' character. Also it could be a string vector such that each element is a ribonucleotide sequence. Eaxh sequence in the training set is associated with a label. The label is found in the parameret labeltr.
`percent`	determines the threshold which is used to identify sequences (in the training set) which are similar to the input sequence.
`labeltr`	This parameter is a vector whose length is equivalent to the number of sequences in the training set. It shows class of each sequence in the trainig set.
`label`	is an optional parameter. It is a vector whose length is equivalent to the number of sequences. It shows the class of each entry (i.e., sequence).

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

Wei,L., Su,R., Luan,S., Liao,Z., Manavalan,B., Zou,Q. and Shi,X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, (2019).

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/pos2RNA51.txt"),alphabet="rna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/neg2RNA51.txt"),alphabet="rna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")


trainSeq<-c(posSeqs,negSeqs)


labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_RNA(seqs=seqs,trainSeq=trainSeq,percent=10,labeltr=labeltr)