Bioinformatics Advance Access originally published online on June 16, 2005
Bioinformatics 2005 21(16):3377-3384; doi:10.1093/bioinformatics/bti544
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Use of extreme patient samples for outcome prediction from gene expression data
Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613
*To whom correspondence should be addressed.
Motivation: Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients' genotypic microarray data, predictions are made to estimate patients' survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients.
Results: We present a new computational method for patient outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favorable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the outcome. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a risk score that falls into one of the special pre-defined risk groups. We apply this method to several public datasets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan–Meier curves. We also show that the idea of selecting only extreme patient samples for training is effective for improving the prediction accuracy when different gene selection methods are used.
Contact: huiqing{at}i2r.a-star.edu.sg
Supplementary information: http://research.i2r.a-star.edu.sg/huiqing/supplementaldata/survival/survival.html
Received on January 4, 2005; revised on May 12, 2005; accepted on June 14, 2005
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