Publications by authors named "Tsun-Chen Lin"
Article Synopsis
- Correct classification of tumor cells is crucial for developing diagnostic systems using microarrays, and this paper proposes a hybrid framework to enhance this process.
- The method combines a binary version of the differential evolution (DE) algorithm with silhouette statistics to effectively select genes and classify different tumor types.
- Experimental results demonstrate that this hybrid approach successfully differentiates between breast and leukemia cancers and aids in identifying potential biomarkers for cancer diagnosis.
Article Synopsis
- - The paper presents a hybrid framework combining a binary differential evolution (DE) algorithm with silhouette filter statistics to enhance tumor classification in microarray diagnostics.
- - This approach aims to improve the selection of genes and effectively classify different types of cancers, specifically breast and leukemia, using microarray data.
- - Experimental results indicate that this hybrid method successfully distinguishes between cancer subtypes and aids in identifying potential biomarkers for cancer diagnosis.
Correct classification and prediction of tumor cells is essential for a successful diagnosis and reliable future treatment. In this study, we aimed at using genetic algorithms for feature selection and proposed silhouette statistics as a discriminant function to distinguish between six subtypes of pediatric acute lymphoblastic leukemia by using microarray with thousands of gene expressions. Our methods have shown a better classification accuracy than previously published methods and obtained a set of genes effective to discriminate subtypes of pediatric acute lymphoblastic leukemia.
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