Mining HIV dynamics using independent component analysis.
BACKGROUND
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.


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


METHODS
Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.
Draghici
Sorin
S
Department of Computer Science, Wayne State University, IA, USA.
Graziano
Frank
F
Kettoola
Samira
S
Sethi
Ishwar
I
Towfic
George
G
D000465:Algorithms; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology / Q000821:virology; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology / Q000821:virology; D003198:Computer Simulation; D003628:Database Management Systems; D006678:HIV / Q000187:drug effects / Q000276:immunology; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics / Q000276:immunology* / Q000503:physiopathology; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D016896:Treatment Outcome; D019562:Viral Load / Q000379:methods