The ability to investigate the transcription of thousands of genes concurrently by using DNA microarrays offers both major scientific opportunities and significant analytical challenges. Here we describe GABRIEL, a rule-based system of computer programs designed to apply domain-specific and procedural knowledge systematically and uniformly for the analysis and interpretation of data from DNA microarrays. GABRIEL'S problem-solving rules direct stereotypical tasks, whereas domain-specific knowledge pertains to gene functions and relationships or to experimental conditions. Additionally, GABRIEL can learn novel rules through genetic algorithms, which define patterns that best match the data being analyzed and can identify groupings in gene expression profiles preordered by chromosomal position or by a nonsupervised algorithm such as hierarchical clustering. GABRIEL subsystems explain the logic that underlies conclusions and provide a graphical interface and interactive platform for the acquisition of new knowledge. The present report compares GABRIEL'S output with published findings in which expert knowledge has been applied post hoc to microarray groupings generated by hierarchical clustering.
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| http://dx.doi.org/10.1073/pnas.251687398 | DOI Listing |
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