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Bioinformatics Vol. 19 no. 8 2003
Pages 981-986
© 2003 Oxford University Press

Mining HIV dynamics using independent component analysis

Sorin Draghici 1, Frank Graziano 2, Samira Kettoola 3, Ishwar Sethi 4 and George Towfic 5,*

1 Department of Computer Science, Wayne State University
2 Univ. of Wisconsin Hospital Madison
3 Department of Computer Science & Software Engineering, UW-Platt, Wisconsin
4 Department of Computer Science and Engineering, Oakland University, Rochester
5 Department of Computer Science, Clarke College, Dubuque, IA, USA

Received on September 26, 2002 ; revised on December 16, 2002 ; accepted on January 13, 2003

Motivation: 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. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the analysis phase offers a powerful advantage when compared with other mathematical procedures that consider the general behavior of HIV dynamics.

Results: The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. The Kohonen Map has been used to eliminate redundant noise in each pattern to produce a reliable data set for the simulation phase. We show that under potent antiretroviral drugs, the value of the CD4+ cells in infected persons decreases gradually by about 11% every 100 days and the levels of the CD8+ cells increase gradually by about 2% every 100 days.

Availability: Executable code and data libraries are available by contacting the corresponding author.

Implementation: Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.

Contact: gtowfic{at}clarke.edu.

* To whom correspondence should be addressed.


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