CiteULike: maryee's Zhao

Assessing reliability of gene clusters from gene expression data.

K Zhang — 2006-12-05T22:53:11-00:00

Funct Integr Genomics, Vol. 1, No. 3. (November 2000), pp. 156-173.

The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest.

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

Yin Liu — 2005-07-19T02:52:32-00:00

Bioinformatics, Vol. 21, No. 15. (1 August 2005), pp. 3279-3285.

Development of human protein reference database as an initial platform for approaching systems biology in humans.

S Peri — 2005-12-19T20:35:19-00:00

Genome Res, Vol. 13, No. 10. (October 2003), pp. 2363-2371.

Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.