Name
Abstract | ||
|---|---|---|
High-throughput technologies such as DNA microarray are in the process of revolutionizing the way modern biological research is being done. Bioinformatics tools are becoming increasingly important to assist biomedical scientists in their quest in understanding complex biological processes. Gene expression analysis has attracted a large amount of attention over the last few years mostly in the form of algorithms, exploring cluster and regulatory relationships among genes of interest, and programs that try to display the multidimensional microarray data in appropriate formats so that they make biological sense. To reduce the dimensionality of microarray data and make the corresponding analysis more biologically relevant, in this paper we propose a biologically-led approach to biochemical pathway analysis using microarray data and relevant biological knowledge. The method selects a subset of genes for each pathway that describes the behaviour of the pathway at a given experimental condition, and transforms them into pathway signatures. The metabolic pathways of Escherichia coli are used as a case study. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.jbi.2007.01.004 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
pathway signature,multidimensional microarray data,metabolic pathway,pathway analysis,microarray data,mining pathway signature,metabolic pathways,microarrays,systems biology,data mining,biological sense,modern biological research,bioinformatics,relevant biological knowledge,dna microarray,complex biological process,escherichia coli,system biology,high throughput,correspondence analysis,biological process,gene expression analysis | Data science,Data mining,Computer science,Systems biology,Microarray analysis techniques,Pathway analysis,Microarray databases,DNA microarray | Journal |
Volume | Issue | ISSN |
40 | 6 | 1532-0480 |
Citations | PageRank | References |
5 | 0.50 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleftherios Panteris | 1 | 6 | 0.90 |
| Stephen Swift | 2 | 427 | 31.32 |
| Annette Payne | 3 | 6 | 0.90 |
| Xiaohui Liu | 4 | 5042 | 269.99 |