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Integrating Statistical and Machine‐Learning Approach for Meta‐Analysis of Bisphenol A‐Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
Abstract
Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta‐analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine‐learning model approach for the meta‐analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co‐expressed genes, respectively.
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Authors (8)
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Nina Lukashina
- Machine Learning Applications and Deep Learning Group, JetBrains Research, Kantemirovskaya st., 2, St. Petersburg 197342, Russia
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Michael Williams
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, BMC, Husargatan 3, P.O. Box 593, 751 24 Uppsala, Sweden
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Elena Kartysheva
- Machine Learning Applications and Deep Learning Group, JetBrains Research, Kantemirovskaya st., 2, St. Petersburg 197342, Russia
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Elizaveta Virko
- Machine Learning Applications and Deep Learning Group, JetBrains Research, Kantemirovskaya st., 2, St. Petersburg 197342, Russia
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Robert Fredriksson
- Uppsala Biomedical Centre, Department of Pharmaceutical Biosciences, Molecular Neuropharmacology, University of Uppsala, Husargatan 3, P.O. Box 591, 751 24 Uppsala, Sweden
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Ola Spjuth
- Uppsala Biomedical Centre, Department of Pharmaceutical Biosciences, Pharmaceutical Bioinformatics, University of Uppsala, Husargatan 3, P.O. Box 591, 751 24 Uppsala, Sweden
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Helgi B. Schiöth
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, BMC, Husargatan 3, P.O. Box 593, 751 24 Uppsala, Sweden
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/ijms221910785
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
no. 22,
ISSN: 1661-6596 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Lukashina N., Williams M., Kartysheva E., Virko E., Kudłak B., Fredriksson R., Spjuth O., Schiöth H. B.: Integrating Statistical and Machine‐Learning Approach for Meta‐Analysis of Bisphenol A‐Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival// INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES -,iss. 22 (2021), s.10785-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/ijms221910785
- Sources of funding:
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- IDUB
- Verified by:
- Gdańsk University of Technology
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