Major advances in genomics and proteomics have led to an explosive growth in the volume of biological information.
The field of bioinformatics can assist both informatics and experimental scientists in optimizing their use of these vast data resources.
We have been active in HIV research since the disease was first discovered.
Our informatics collaborators are computational scientists in The Data Analytical Bioinformatics Core at the NY State Center of Excellence in
Bioinformatics and the Life Sciences (CoE) and The Buffalo Center for Biomedical Computing (BCBC)
of The Department of Computer Science and Engineering at UB;
the BCBC is a member of the NIH supported National Programs of Excellence in Biomedical Computing.
We will analyze large, complex, clinical, genomic and proteomic database that we are building from our
HIV-1 infected patients.
This project, using data mining methods, will contribute to a fundamental understanding of the pathogenesis of HIV infections and identify new markers of
disease progression and potential new targets for therapy.
We focus on a unique cohort of HIV-1 infected patients who are long term non-progressors (LTNP) in comparison with normal progressors (NP).
Susceptibility to and progression of HIV-1 infection is dependent on different virus and host factors (Yue et al.,2005; Campbell et al., 2004.,
Anzala et al., 1995; Cao et al., 1995; Buchbinder et al., 1994).
Host genetic factors such as human allelic variants, and HLA and HIV co-receptor polymorphisms significantly influence disease outcomes.
As the course of an HIV-1 infection is a complex, orchestrated action of many different mechanisms,
we propose that a database containing clinical, genomic and proteomic data comparing different patient cohorts may yield significant new biomarkers for
susceptibility to HIV-1 infection and disease progression.
Thus we hypothesize that using innovative informatics technology we shall develop a large, interactive database containing clinical, genomic and proteomic
information on HIV-1 infected patients that can be used to guide rationale, evidence-based, decision making at both the clinical and public health levels.