Encoding of Nucleotide Sequences into Numeric Feature Vectors

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Documentation for package ‘EncDNA’ version 1.0.2

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APR.Feature Adjacent position relationship feature.
Bayes.Feature Projecting nucleotide sequences into numeric feature vectors using Bayes kernel encoding approach.
Density.Feature Nucleotide sequence encoding with the distribution of trinucleotides.
droso An example dataset consisting of true and false donor splice sites of Drosophila melanogaster.
Maldoss.Feature Encoding of nucleic acid sequences using di-nucleotide frequency difference between positive and negative class datasets.
MM1.Feature Transforming nucleotide sequences into numeric vectors using first order nucleotide dependency.
MM2.Feature Mapping nucleotide sequences onto numeric feature vectors based on second order nucleotide dependencies.
MN.Fdtf.Feature Sequence encoding with nucleotide frequency difference between two classes of sequence datasets.
PN.Fdtf.Feature Conversion of nucleotide sequences into numeric feature vectors based on the difference of dinucleotide frequency.
POS.Feature Transformation of nucleic acid sequences into numeric vectors using position-wise frequency of nucleotides.
Predoss.Feature Encoding nucleotide sequences using all possible di-nucleotide dependencies.
SAE.Feature Encoding of nucleotide sequences based on sum of absolute error (SAE) of each sequence.
Sparse.Feature Nucleotide sequence encoding with 0 and 1.
Trint.Dist.Feature Tri-nucleotide distribution-based encoding of nucleotide sequences.
WAM.Feature Nucleic acid sequence encoding based on weighted array model.
WMM.Feature Weighted matrix model based mapping of nucleotide sequences into vectors of numeric observations.