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

Authors:
S. Sathiya Keerthi;Chong Jin Ong;Keng Boon Siah;David B. L. Lim;Wei Chu;Min Shi;David S. Edwin;Rakesh Menon;Lixiang Shen;Jonathan Y. K. Lim;Han Tong Loh
Affiliations:
National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2002

Citing 1
Cited 2

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition

Classifying biological articles using web resources

Proceedings of the 2004 ACM symposium on Applied computing
Feature generation and representations for protein-protein interaction classification

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

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.