Imperial College London
Alternate

DrStefanoCacciatore

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 2137s.cacciatore

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cacciatore:2014:10.1073/pnas.1220873111,
author = {Cacciatore, S and Luchinat, C and Tenori, L},
doi = {10.1073/pnas.1220873111},
journal = {Proceedings of the National Academy of Sciences},
pages = {5117--5122},
title = {Knowledge discovery by accuracy maximization},
url = {http://dx.doi.org/10.1073/pnas.1220873111},
volume = {111},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Significance</jats:title>          <jats:p>We propose an innovative method to extract new knowledge from noisy and high-dimensional data. Our approach differs from previous methods in that it has an integrated procedure of validation of the results through maximization of cross-validated accuracy. In many cases, this method performs better than existing feature extraction methods and offers a general framework for analyzing any kind of complex data in a broad range of sciences. Examples ranging from genomics and metabolomics to astronomy and linguistics show the versatility of the method.</jats:p>
AU  - Cacciatore,S
AU  - Luchinat,C
AU  - Tenori,L
DO  - 10.1073/pnas.1220873111
EP  - 5122
PY  - 2014///
SN  - 0027-8424
SP  - 5117
TI  - Knowledge discovery by accuracy maximization
T2  - Proceedings of the National Academy of Sciences
UR  - http://dx.doi.org/10.1073/pnas.1220873111
VL  - 111
ER  -
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