Machine Learning
Interior-Point Methods for Massive Support Vector Machines
SIAM Journal on Optimization
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature-guided clustering of multi-dimensional flow cytometry datasets
Journal of Biomedical Informatics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Malignant neutrophils of chronic myelogenous leukemia (CML) have similar antigen expression patterns compared to their normal counterparts, thus making the cells difficult to distinguish by clinical flow cytometry. In this study, we applied the support vector machine method to build a malignant neutrophil prediction model based on nine CML patients and nine healthy donors. This approach effectively differentiated between malignant and normal neutrophils with high specificity and sensitivity (@?95.80% and @?95.30%, respectively). This approach may broaden the application of flow cytometry for differentiation between CML and normal neutrophils and become an important diagnostic tool in CML.