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Tumor Classification Using Phylogenetic Methods on Expression Data by Richard Desper; Javed Khan; Alejandro A. Schäffer is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Tumor Classification Using Phylogenetic Methods on Expression Data about?

Tumor classification is a well-studied problem in the field of bioinformatics. Developments in the field of DNA chip design have now made it possible to measure the expression levels of thousands of genes in sample tissue from healthy cell lines or tumors. A number of studies have examined the problems of tumor classification: class discovery, the problem of defining a number of classes of tumors using the data from a DNA chip, and class prediction, the problem of accurately classifying an unknown tumor, given expression data from the unknown tumor and from a learning set. The current work has applied phylogenetic methods to both problems. To solve the class discovery problem, we impose a metric on a set of tumors as a function of their gene expression levels, and impose a tree structure on this metric, using standard tree fitting methods borrowed from the field of phylogenetics. Phylogenetic methods provide a simple way of imposing a clear hierarchical relationship on the data, with branch lengths in the classification tree representing the degree of separation witnessed. We tested our method for class discovery on two data sets: a data set of 87 tissues, comprised mostly of small

Who reads Tumor Classification Using Phylogenetic Methods on Expression Data?

Author

Richard Desper; Javed Khan; Alejandro A. Schäffer

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 0022-5193)

Published

2004

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)