Abstract:
We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. T...Show MoreMetadata
Abstract:
We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. By converting the sequences of nucleotides and polypeptides into digital genomic signals, this approach offers the possibility to use a large variety of signal processing methods for their handling and analysis. It is also shown that some essential features of the nucleotide sequences can be better extracted using this representation. New tools for genomic signal analysis, including the use of phase, aggregated phase, unwrapped phase, sequence path, stem representation of components' relative frequencies, as well as analysis of the transitions are introduced at the nucleotide, codon and amino acid levels, and in a multiresolution approach.
Published in: 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)
Date of Conference: 18-18 December 2010
Date Added to IEEE Xplore: 28 January 2011
ISBN Information: