MOTIVATION: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. RESULTS: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.
MOTIVATION: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. RESULTS: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.
Authors: Javier Herrero; Fátima Al-Shahrour; Ramón Díaz-Uriarte; Alvaro Mateos; Juan M Vaquerizas; Javier Santoyo; Joaquín Dopazo Journal: Nucleic Acids Res Date: 2003-07-01 Impact factor: 16.971
Authors: Hong Pan; Li Zuo; Vidhu Choudhary; Zhuo Zhang; Shoi Houi Leow; Fui Teen Chong; Yingliang Huang; Victor Wui Siong Ong; Bijayalaxmi Mohanty; Sin Lam Tan; S P T Krishnan; Vladimir B Bajic Journal: Nucleic Acids Res Date: 2004-07-01 Impact factor: 16.971
Authors: Chad Creighton; Rork Kuick; David E Misek; David S Rickman; Franck M Brichory; Jean-Marie Rouillard; Gilbert S Omenn; Samir Hanash Journal: Genome Biol Date: 2003-06-23 Impact factor: 13.583