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An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge
July-September 2010 (vol. 7 no. 3)
pp. 454-461
ASCII Text
x 
 
Yonggang Cao, Zuofeng Li, Feifan Liu, Shashank Agarwal, Qing Zhang, Hong Yu, "An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 454-461, July-September, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2010.56,
author = {Yonggang Cao and Zuofeng Li and Feifan Liu and Shashank Agarwal and Qing Zhang and Hong Yu},
title = {An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {7},
number = {3},
issn = {1545-5963},
year = {2010},
pages = {454-461},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.56},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge
IS - 3
SN - 1545-5963
SP454
EP461
EPD - 454-461
A1 - Yonggang Cao,
A1 - Zuofeng Li,
A1 - Feifan Liu,
A1 - Shashank Agarwal,
A1 - Qing Zhang,
A1 - Hong Yu,
PY - 2010
KW - Bioinformatics (genome or protein) databases
KW - information search and retrieval
KW - systems and software
KW - text mining.
VL - 7
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Yonggang Cao, University of Wisconsin-Milwaukee, Milwaukee
Zuofeng Li, University of Wisconsin-Milwaukee, Milwaukee
Feifan Liu, University of Wisconsin-Milwaukee, Milwaukee
Shashank Agarwal, University of Wisconsin-Milwaukee, Milwaukee
Qing Zhang, University of Wisconsin-Milwaukee, Milwaukee
Hong Yu, University of Wisconsin-Milwaukee, Milwaukee
The team at the University of Wisconsin-Milwaukee developed an information retrieval and machine learning framework. Our framework requires only the standardized training data and depends upon minimal external knowledge resources and minimal parsing. Within the framework, we built our text mining systems and participated for the first time in all three BioCreative II.5 Challenge tasks. The results show that our systems performed among the top five teams for raw F1 scores in all three tasks and came in third place for the homonym ortholog F1 scores for the INT task. The results demonstrated that our IR-based framework is efficient, robust, and potentially scalable.

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Index Terms:
Bioinformatics (genome or protein) databases, information search and retrieval, systems and software, text mining.
Citation:
Yonggang Cao, Zuofeng Li, Feifan Liu, Shashank Agarwal, Qing Zhang, Hong Yu, "An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 454-461, July-Sept. 2010, doi:10.1109/TCBB.2010.56
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