A machine learning approach for the curation of biomedical literature

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A machine learning approach for the curation of biomedical literature: KDD Cup 2002 (task 1)

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ACM SIGKDD Explorations Newsletter archive
Volume 4 , Issue 2 (December 2002) table of contents

Pages: 93 - 94

Year of Publication: 2002

ISSN:1931-0145

Authors

S. Sathiya Keerthi	National University of Singapore, Singapore
Chong Jin Ong	National University of Singapore, Singapore
Keng Boon Siah	National University of Singapore, Singapore
David B. L. Lim	National University of Singapore, Singapore
Wei Chu	National University of Singapore, Singapore
Min Shi	National University of Singapore, Singapore
David S. Edwin	National University of Singapore, Singapore
Rakesh Menon	National University of Singapore, Singapore
Lixiang Shen	National University of Singapore, Singapore
Jonathan Y. K. Lim	National University of Singapore, Singapore
Han Tong Loh	National University of Singapore, Singapore

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ACM New York, NY, USA

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ABSTRACT

In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naïve Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources.

REFERENCES

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	1	Jie Cheng , Russell Greiner, Learning Bayesian Belief Network Classifiers: Algorithms and System, Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence, p.141-151, June 07-09, 2001
	2	BRADLEY, A.P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145--1159