Academic Journal
Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish ( danio rerio )
| العنوان: | Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish ( danio rerio ) |
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| المؤلفون: | Wang Rong-Lin, Bencic David, Biales Adam, Flick Robert, Lazorchak Jim, Villeneuve Daniel, Ankley Gerald T |
| المصدر: | BMC Genomics, Vol 13, Iss 1, p 358 (2012) |
| بيانات النشر: | BMC |
| سنة النشر: | 2012 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Gene classifiers, Endocrine-disrupting chemicals, Transcriptomics, Mechanism of action, Zebrafish, Biotechnology, TP248.13-248.65, Genetics, QH426-470 |
| الوصف: | Background Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1471-2164 |
| Relation: | http://www.biomedcentral.com/1471-2164/13/358; https://doaj.org/toc/1471-2164; https://doaj.org/article/d61ec67ead1e413b8912b683edc0793b |
| DOI: | 10.1186/1471-2164-13-358 |
| الاتاحة: | https://doi.org/10.1186/1471-2164-13-358 https://doaj.org/article/d61ec67ead1e413b8912b683edc0793b |
| رقم الانضمام: | edsbas.CE8009FF |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14712164 |
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| DOI: | 10.1186/1471-2164-13-358 |