An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of Artificial Neural Networks.
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%0 Journal Article
%1 journals/ijon/FloridoPRGGU13
%A Perez-Florido, Javier
%A Pomares, Héctor
%A Rojas, Ignacio
%A Guillén, Alberto
%A Guzman, Francisco M. Ortuño
%A Urquiza, José M.
%D 2013
%J Neurocomputing
%K dblp
%P 64-78
%T An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of Artificial Neural Networks.
%U http://dblp.uni-trier.de/db/journals/ijon/ijon121.html#FloridoPRGGU13
%V 121
@article{journals/ijon/FloridoPRGGU13,
added-at = {2023-12-17T00:00:00.000+0100},
author = {Perez-Florido, Javier and Pomares, Héctor and Rojas, Ignacio and Guillén, Alberto and Guzman, Francisco M. Ortuño and Urquiza, José M.},
biburl = {https://www.bibsonomy.org/bibtex/2d1a237cdc33b20b893dd8d7e579f89f2/dblp},
ee = {https://doi.org/10.1016/j.neucom.2012.11.040},
interhash = {6fea0c8136e452d84e16efea0fc15eb5},
intrahash = {d1a237cdc33b20b893dd8d7e579f89f2},
journal = {Neurocomputing},
keywords = {dblp},
pages = {64-78},
timestamp = {2024-04-08T19:55:21.000+0200},
title = {An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of Artificial Neural Networks.},
url = {http://dblp.uni-trier.de/db/journals/ijon/ijon121.html#FloridoPRGGU13},
volume = 121,
year = 2013
}
