Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This p
From global expression data to gene networks
β Scribed by Denis Thieffry
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 100 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0265-9247
No coin nor oath required. For personal study only.
β¦ Synopsis
Allowing the parallel monitoring of the transcription of thousands of genes, microarrays constitute a powerful technique for functional genomics. In a recent paper, a clustering method and a local alignment software were combined to identify DNA motifs in sets of yeast genes endowed with similar transcription profiles throughout mitosis (1) . Identifying various known transcriptional binding sites together with new putative ones, the authors made a significant step towards a systematic characterization of the regulatory structure of genomic networks.
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A gene regulatory network depicts which genes turn on which and at what moment. Knowledge of such gene networks is key to an understanding of the biological process. We propose here to use a statistical method for the reconstruction of gene regulatory networks based on Bayesian networks from microar