A Novel Visualization Classifier and Its Applications.
ABSTRACT Classifiers, as one of the important tools of analyzing gene expression data in the post-genomic epoch, have been used widely
in the classification of different cancer types in the past few years. Although most existing classifiers have high classification
accuracy, the process of classification is a black box and they can not give biologists more information and interpretable
results of classification. In this paper, we propose a novel visualization cancer classification method. Besides offering
high classification accuracy, the method can help us identify complex disease-related genes and assess gene expression variation
during the process of classification. The results of classification are natural and interpretable and the process of classification
is visible. To evaluate the performance of the method we have applied the proposed method to three public data sets. The experimental
results demonstrate that the approach is feasible and useful.
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ABSTRACT: Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in part because of the lack of effective early detection methods. Although alterations of several genes, such as c-erb-B2, c-myc, and p53, have been identified in a significant fraction of ovarian cancers, none of these mutations are diagnostic of malignancy or predictive of tumor behavior over time. Here, we used oligonucleotide microarrays with probe sets complementary to >6,000 human genes to identify genes whose expression correlated with epithelial ovarian cancer. We extended current microarray technology by simultaneously hybridizing ovarian RNA samples in a highly parallel manner to a single glass wafer containing 49 individual oligonucleotide arrays separated by gaskets within a custom-built chamber (termed "array-of-arrays"). Hierarchical clustering of the expression data revealed distinct groups of samples. Normal tissues were readily distinguished from tumor tissues, and tumors could be further subdivided into major groupings that correlated both to histological and clinical observations, as well as cell type-specific gene expression. A metric was devised to identify genes whose expression could be considered ideal for molecular determination of epithelial ovarian malignancies. The list of genes generated by this method was highly enriched for known markers of several epithelial malignancies, including ovarian cancer. This study demonstrates the rapidity with which large amounts of expression data can be generated. The results highlight important molecular features of human ovarian cancer and identify new genes as candidate molecular markers.Proceedings of the National Academy of Sciences 01/2001; 98(3):1176-81. · 9.74 Impact Factor
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ABSTRACT: We describe the use of singular value decomposition in transforming genome-wide expression data from genesProc SPIE 12/2001;
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ABSTRACT: We used reverse transcription-coupled PCR to produce a high-resolution temporal map of fluctuations in mRNA expression of 112 genes during rat central nervous system development, focusing on the cervical spinal cord. The data provide a temporal gene expression "fingerprint" of spinal cord development based on major families of inter- and intracellular signaling genes. By using distance matrices for the pair-wise comparison of these 112 temporal gene expression patterns as the basis for a cluster analysis, we found five basic "waves" of expression that characterize distinct phases of development. The results suggest functional relationships among the genes fluctuating in parallel. We found that genes belonging to distinct functional classes and gene families clearly map to particular expression profiles. The concepts and data analysis discussed herein may be useful in objectively identifying coherent patterns and sequences of events in the complex genetic signaling network of development. Functional genomics approaches such as this may have applications in the elucidation of complex developmental and degenerative disorders.Proceedings of the National Academy of Sciences 02/1998; 95(1):334-9. · 9.74 Impact Factor