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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

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Machine Learning Applications and Deep Learning Group, JetBrains Research, Kantemirovskaya Str., 2, 197342 St. Petersburg, Russia
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Department of Neuroscience, Functional Pharmacology, University of Uppsala, BMC, Husargatan 3, Box 593, 751 24 Uppsala, Sweden
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Information Technologies and Programming Faculty, ITMO University, Kronverksky Pr. 49, bldg. A, 197101 St. Petersburg, Russia
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St. Petersburg School of Physics, Mathematics, and Computer Science, HSE University, 16 Soyuza Pechatnikov Street, 190121 St. Petersburg, Russia
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Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, 11/12 Narutowicza Str., 80-233 Gdańsk, Poland
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Department of Pharmaceutical Biosciences, Molecular Neuropharmacology, Uppsala Biomedical Centre, University of Uppsala, Husargatan 3, Box 591, 751 24 Uppsala, Sweden
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Department of Pharmaceutical Biosciences, Pharmaceutical Bioinformatics, Uppsala Biomedical Centre, University of Uppsala, Husargatan 3, Box 591, 751 24 Uppsala, Sweden
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Institute of Translational Medicine and Biotechnology, I. M. Sechenov First Moscow State Medical University, Trubetskay Str. 8, bldg 2, 119991 Moscow, Russia
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Author to whom correspondence should be addressed.
Academic Editors: Ashis Basu and Anthony Lemarié
Int. J. Mol. Sci. 2021, 22(19), 10785; https://doi.org/10.3390/ijms221910785
Received: 1 September 2021 / Revised: 23 September 2021 / Accepted: 27 September 2021 / Published: 5 October 2021
(This article belongs to the Section Molecular Toxicology)
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. By applying machine learning methods to these datasets, we identified genes whose expression was significantly affected in all of the BPA microanalysis data tested; those involved in the regulation of cell survival include: Tnfr2, Hgf-Met, Agtr1a, Bdkrb2; signaling through Mapk8 (Jnk1)); DNA repair (Hgf-Met, Mgmt); apoptosis (Tmbim6, Bcl2, Apaf1); and cellular junctions (F11r, Cldnd1, Ctnd1 and Yes1). Our results highlight the benefit of combining existing datasets for the integrated analysis of a specific topic when individual datasets are limited in size. View Full-Text
Keywords: BPA; BPA-exposure datasets; DNA repair; cellular junction BPA; BPA-exposure datasets; DNA repair; cellular junction
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MDPI and ACS Style

Lukashina, N.; Williams, M.J.; 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. Int. J. Mol. Sci. 2021, 22, 10785. https://doi.org/10.3390/ijms221910785

AMA Style

Lukashina N, Williams MJ, Kartysheva E, Virko E, Kudłak B, Fredriksson R, Spjuth O, Schiöth HB. 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. 2021; 22(19):10785. https://doi.org/10.3390/ijms221910785

Chicago/Turabian Style

Lukashina, Nina, Michael J. Williams, Elena Kartysheva, Elizaveta Virko, Błażej Kudłak, Robert Fredriksson, Ola Spjuth, and Helgi B. Schiöth. 2021. "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 22, no. 19: 10785. https://doi.org/10.3390/ijms221910785

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