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Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)
HIV-1 Coreceptor Usage Prediction via Indexed Local Kernel Smoothing Methods and Grid-Based Multiple Statistical Validation
Maribor, Slovenia
June 20-June 22
ISBN: 0-7695-2905-4
ASCII Text
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Iuri Fanti, Mattia CF Prosperi, Giovanni Ulivi, Alessandro Micarelli, "HIV-1 Coreceptor Usage Prediction via Indexed Local Kernel Smoothing Methods and Grid-Based Multiple Statistical Validation," Computer-Based Medical Systems, IEEE Symposium on, pp. 465-470, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007.
 
 
BibTex
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@article{ 10.1109/CBMS.2007.55,
author = {Iuri Fanti and Mattia CF Prosperi and Giovanni Ulivi and Alessandro Micarelli},
title = {HIV-1 Coreceptor Usage Prediction via Indexed Local Kernel Smoothing Methods and Grid-Based Multiple Statistical Validation},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2007},
issn = {1063-7125},
pages = {465-470},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2007.55},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - HIV-1 Coreceptor Usage Prediction via Indexed Local Kernel Smoothing Methods and Grid-Based Multiple Statistical Validation
SN - 1063-7125
SP465
EP470
A1 - Iuri Fanti,
A1 - Mattia CF Prosperi,
A1 - Giovanni Ulivi,
A1 - Alessandro Micarelli,
PY - 2007
KW - null
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Iuri Fanti, University of Roma TRE, Italy
Mattia CF Prosperi, University of Roma TRE, Italy
Giovanni Ulivi, University of Roma TRE, Italy
Alessandro Micarelli, University of Roma TRE, Italy
Human immunodefieciency virus type 1 (HIV-1) isolates differ in their use of coreceptors to enter target cells. This has important implications for both viral pathogenicity and susceptibility to entry inhibitors under development. Predicting HIV-1 coreceptor usage on the basis of sequence information is a challenging task due to the high variability of the HIV-1 genome. We present an efficient local smoothing kernel method, enhanced with a BLAST-based distance function, implemented by usage of multithreading grid procedures and indexing. Robust validation of the model is achieved through multiple cross-validation, along with statistical comparisons of results for performance assessment.
Citation:
Iuri Fanti, Mattia CF Prosperi, Giovanni Ulivi, Alessandro Micarelli, "HIV-1 Coreceptor Usage Prediction via Indexed Local Kernel Smoothing Methods and Grid-Based Multiple Statistical Validation," cbms, pp.465-470, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007
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