Dataset Information

Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge.

ABSTRACT: 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.

SUBMITTER: Pan KH

PROVIDER: S-EPMC122328 | biostudies-literature | 2002 Feb

REPOSITORIES: biostudies-literature

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Publications

Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge.

Pan Kuang-Hung KH Lih Chih-Jian CJ Cohen Stanley N SN

Proceedings of the National Academy of Sciences of the United States of America 20020201 4

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 knowledg ...[more]

PMID: 11854507

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Project description:BackgroundThe explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation.ResultsOver 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from >/=88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections.ConclusionTo overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field.

Dataset Information

Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge.

Publications

Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge.

Similar Datasets

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