Encoding of Nucleotide Sequences into Numeric Feature Vectors

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Documentation for package ‘EncDNA’ version 1.0.2

• DESCRIPTION file.

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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.