Associations between gene expressions in breast cancer and patient survival.

We analyzed associations between gene expression in breast cancer and patient survival for 8024 genes from a previously published microarray data set. Analysis of survival, by using the logrank test, was performed automatically for each gene. After correcting for multiple testing, we identified 95 genes whose expression was significantly associated with patient survival. The independent prognostic value of the genes ranking the highest in univariate analysis, together with clinical parameters, was assessed by Cox multivariate regression analysis. The P-values from these logrank tests were also mapped to chromosomal positions and compared with previously reported amplicon regions. We used PubGene web tools to identify groups of genes that had co-occurred in the literature and whose expression patterns were associated with survival. Our analyses demonstrate the comprehensiveness of the microarray technology with respect to measuring gene expression and indicate that the technology may be used to screen for potential clinical markers.

[New knowledge derived from measurement of gene expression with the DNA microarray method]. / Ny kunnskap fra måling av genuttrykk gjennom DNA-mikromatrise.

BACKGROUND: The cDNA microarray method offers the first possibility of obtaining a global understanding of biological processes in living organisms, by simultaneous read-outs of tens of thousands of mRNAs. Initial experiments suggest that genes with similar function have similar expression patterns. MATERIAL AND METHODS: Understanding this level of biological complexity will, however, require completely new approaches to data analysis. Computer science methods, such as data mining and knowledge discovery, can synthesize interpretable if-then rules that model the relation between gene expressions and functions and use the rules to classify unknown genes. The huge body of existing biological and medical knowledge makes it necessary to develop methods for extracting knowledge from such repositories. RESULTS: Models of relations between gene expressions and gene functions in a data set from a publicly available source are synthesized semiautomatically and applied to classify unknown genes. Encouraging results have been achieved. The method is applied in the analysis of data from our microarray system which has recently become operational. INTERPRETATION: The principles are of general importance and will be used to evaluate a wide range of complex data sets like decision support in clinical medicine, for situations in which physicians need to handle a large volume of data for each patient.

A literature network of human genes for high-throughput analysis of gene expression.

We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets.

A set-covering approach to specific search for literature about human genes.

With the advent of the cDNA microarray and oligonucleotide array technologies it has become possible to study a large number of genes in a single experiment. While experiments with thousands of genes are routinely performed, searching for literature about several genes by traditional methods is time consuming and error-prone. In addition to the inherent limitations of free text search, use of the conventional Boolean operators often result in either none (when AND'ing terms) or far too many (when OR'ing terms) hits. We have created a two-step procedure as an approach to meeting the challenge of multi-gene queries. Our results so far shows that the returned sets of articles scores high on relevance.