loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)
Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1
Maribor, Slovenia
June 20-June 22
ISBN: 0-7695-2905-4
ASCII Text
x 
 
Mattia CF Prosperi, Giovanni Ulivi, Maurizio Zazzi, "Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1," Computer-Based Medical Systems, IEEE Symposium on, pp. 427-432, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007.
 
 
BibTex
x
 
@article{ 10.1109/CBMS.2007.100,
author = {Mattia CF Prosperi and Giovanni Ulivi and Maurizio Zazzi},
title = {Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2007},
issn = {1063-7125},
pages = {427-432},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2007.100},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1
SN - 1063-7125
SP427
EP432
A1 - Mattia CF Prosperi,
A1 - Giovanni Ulivi,
A1 - Maurizio Zazzi,
PY - 2007
KW - null
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Mattia CF Prosperi, University of Roma TRE, Italy
Giovanni Ulivi, University of Roma TRE, Italy
Maurizio Zazzi, University of Siena, Italy
Predicting the in-vivo effect of genotypic drug resistance of Human Immunodeficiency Virus type-1 (HIV-1) on response to antiretroviral therapies represents a major clinical issue. Different machine learning and feature selection methods are applied for the classification of treatment success, based on viral genotype, therapy and derived input features. The robustness of results is assessed through statistical validation. The procedures described are intended to be a general methodology in the challenging context of biology and medical science data mining.
Citation:
Mattia CF Prosperi, Giovanni Ulivi, Maurizio Zazzi, "Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1," cbms, pp.427-432, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007
Usage of this product signifies your acceptance of the Terms of Use.