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2009 International Conference on Advanced Information Networking and Applications Workshops
Machine Learning Applied to BRCA1 Hereditary Breast Cancer Data
Bradford, United Kingdom
May 26-May 29
ISBN: 978-0-7695-3639-2
ASCII Text
x 
 
Andrei Doncescu, Baptiste Tauzain, Nabil Kabbaj, "Machine Learning Applied to BRCA1 Hereditary Breast Cancer Data," Advanced Information Networking and Applications Workshops, International Conference on, pp. 942-947, 2009 International Conference on Advanced Information Networking and Applications Workshops, 2009.
 
 
BibTex
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@article{ 10.1109/WAINA.2009.165,
author = {Andrei Doncescu and Baptiste Tauzain and Nabil Kabbaj},
title = {Machine Learning Applied to BRCA1 Hereditary Breast Cancer Data},
journal ={Advanced Information Networking and Applications Workshops, International Conference on},
volume = {0},
year = {2009},
isbn = {978-0-7695-3639-2},
pages = {942-947},
doi = {http://doi.ieeecomputersociety.org/10.1109/WAINA.2009.165},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Advanced Information Networking and Applications Workshops, International Conference on
TI - Machine Learning Applied to BRCA1 Hereditary Breast Cancer Data
SN - 978-0-7695-3639-2
SP942
EP947
A1 - Andrei Doncescu,
A1 - Baptiste Tauzain,
A1 - Nabil Kabbaj,
PY - 2009
KW - breast cancer
KW - BRCA1
KW - inductive logic programming
KW - Heredity
KW - polymorphism
VL - 0
JA - Advanced Information Networking and Applications Workshops, International Conference on
ER -
 
This research aims to provide a tool to doctors in order to help for diagnosis of BRCA1 hereditary breast cancer. Our goal is to determine, if possible, profiles that are responsible for early cancer onset. In order to extract knowledge from the biological information above we will create a relational database that will allow prognosticating cancer apparition. We want to determine different types responsible for different profiles of cancer onset thanks to machine learning programs. The prognostic will rely on polymorphisms of a gene, BRCA1, but on family history as well. The machine learning software(s) will be used as a tool by doctors as a help for diagnosis. This tool will be used in order to determine if these patients are member of a high risk cluster, an early occurring cancer.
Index Terms:
breast cancer, BRCA1, inductive logic programming, Heredity, polymorphism
Citation:
Andrei Doncescu, Baptiste Tauzain, Nabil Kabbaj, "Machine Learning Applied to BRCA1 Hereditary Breast Cancer Data," waina, pp.942-947, 2009 International Conference on Advanced Information Networking and Applications Workshops, 2009
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