The College of Life Sciences (CLS) remains one of the most prestigious—and the oldest—colleges in Zhejiang University. This special issue, which includes 16 reviews contributed by our alumni and faculties, is dedica...The College of Life Sciences (CLS) remains one of the most prestigious—and the oldest—colleges in Zhejiang University. This special issue, which includes 16 reviews contributed by our alumni and faculties, is dedicated to mark the 90th Anniversary of CLS.展开更多
Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology...Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.展开更多
文摘The College of Life Sciences (CLS) remains one of the most prestigious—and the oldest—colleges in Zhejiang University. This special issue, which includes 16 reviews contributed by our alumni and faculties, is dedicated to mark the 90th Anniversary of CLS.
文摘Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.
