MedMeSH summarizer: text mining for gene clusters

BMC bioinformatics, 2005

High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining--the process of representing literature data in a fashion that is easy to relate to genomic data--is one solution to this problem. We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands th...

…, 2003

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

2003

We describe the current status of the gene expression database CIBEX (Center for Information Biology gene EXpression database, http://cibex.nig.ac.jp), with a data retrieval system in compliance with MIAME, a standard that the MGED Society has developed for comparing and data produced in microarray experiments at different laboratories worldwide. CIBEX serves as a public repository for a wide range of high-throughput experimental data in gene expression research, including microarraybased experiments measuring mRNA, serial analysis of gene expression (SAGE tags), and mass spectrometry proteomic data. To cite this article: K. Ikeo et al., C. R. Biologies 326 (2003).  2003 Published by Elsevier SAS on behalf of Académie des sciences. Résumé CIBEX : base de données d'expression génique du Centre d'information biologique. Nous décrivons l'état actuel de la base de données d'expression génique CIBEX (Center for Information Biology gene EXpression database, http://cibex.nig.ac.jp), associée à un système d'interrogation compatible avec MIAME, standard développé par la société MGED dans le but de comparer les données issues d'expériences utilisant les microréseaux des laboratoires du monde entier. CIBEX sert de conservatoire public pour une large gamme de données expérimentales issues de la recherche à haut débit sur l'expression des gènes, incluant la mesure des ARNm à l'aide de microréseaux, l'analyse en série de l'expression génique (étiquettes SAGE) et des données protéomiques obtenues par spectrométrie de masse.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

Cytometry, 2002

Nucleic Acids Research, 2010

International Journal of Medical Informatics, 2008

Genome Informatics …, 2000

Expression arrays facilitate the monitoring of changes in expression patterns of large collections of genes. It is generally expected that genes with similar expression patterns would correspond to proteins of common biological function. We assess this common assumption by comparing levels of similarity of expression patterns and statistical significance of biological terms that describe the corresponding protein functions. Terms are automatically obtained by mining large collections of Medline abstracts. We propose that the combined use of the tools for expression profiles clustering and automatic function retrieval, can be useful tools for the detection of biologically relevant associations between genes in complex gene expression experiments. The results obtained using publicly available experimental data show how, in general, an increase in the similarity of the expression patterns is accompanied by an enhancement of the amount of specific functional information or, in other words, how the selected terms became more specific following an increase in the specificity of the expression patterns. Particularly interesting are the discrepancies from this general trend, i.e. groups of genes with similar expression patterns but very little in common at the functional level. In these cases the similarity of their expression profiles becomes the first link between previously unrelated genes.

Genome …, 2001

Journal of biomolecular techniques : JBT, 2002

A series of successful microarray experiments typically results in a data matrix containing a list of a few thousand genes and their measured expression values across various conditions tested. The understanding of why some genes are clustered in their expression pattern can be greatly facilitated by gathering all prior knowledge about all the genes in a given cluster. Here we demonstrate a strategy using the NCI60 data set (Scherf U, Ross DT,Waltham M, et al., Nature Genet 2000;24(3):236-244) on a cluster of genes highly expressed in the melanoma cell lines. By traversing public databases using cross-references between the databases,we show that the Gene Ontology (GO) classifications of some of the genes are consistent with their products' involvement in melanin biosynthesis.