Support Vector Machine approach for cancer detection using Amplified Fragment length Polymorphism (AFLP) screening method

Authors:
Waiming Kong;Lawrence Tham;Kee Yew Wong;Patrick Tan
Affiliations:
Nanyang Polytechnic, Singapore;Nanyang Polytechnic, Singapore;National Cancer Centre of Singapore, Singapore;National Cancer Centre of Singapore, Singapore
Venue:
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Year:
2004

Citing 4
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Making large-scale support vector machine learning practical

Advances in kernel methods
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
New Methods for Splice Site Recognition

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks

Intelligible support vector machines for diagnosis of diabetes mellitus

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support Vector Machine is used to classify data obtained from Amplified Fragment length Polymorphism screening of gastric cancer and normal tissue samples. Using the electrophoresis peak intensity measurements of the amplified fragments of the cancer and normal tissues, SVM was able to distinguish gastric cancer from normal tissue samples with a senssitivity of 0.98 and specificity of 0.75. As AFLP is a low cost procedure which requires minimum prior sequence knowledge and biological material, SVM prediction of AFLP screening data is a potential tool for gastric cancer screening and diagnosis.