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Integrating Statist...
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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- Lukashina, Nina (author)
- JetBrains Res, Machine Learning Applicat & Deep Learning Grp, Kantemirovskaya Str 2, St Petersburg 197342, Russia.
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- Williams, Michael J. (author)
- Uppsala universitet,Schiöth: Funktionell farmakologi
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- Kartysheva, Elena (author)
- JetBrains Res, Machine Learning Applicat & Deep Learning Grp, Kantemirovskaya Str 2, St Petersburg 197342, Russia.;ITMO Univ, Informat Technol & Programming Fac, Kronverksky Pr 49,Bldg A, St Petersburg 197101, Russia.
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- Virko, Elizaveta (author)
- JetBrains Res, Machine Learning Applicat & Deep Learning Grp, Kantemirovskaya Str 2, St Petersburg 197342, Russia.;HSE Univ, St Petersburg Sch Phys Math & Comp Sci, 16 Soyuza Pechatnikov St, St Petersburg 190121, Russia.
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- Kudlak, Blazej (author)
- Gdansk Univ Technol, Fac Chem, Dept Analyt Chem, 11-12 Narutowicza Str, PL-80233 Gdansk, Poland.
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- Fredriksson, Robert (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
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- Spjuth, Ola, Professor, 1977- (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
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- Schiöth, Helgi B. (author)
- Uppsala universitet,Schiöth: Funktionell farmakologi,IM Sechenov First Moscow State Med Univ, Inst Translat Med & Biotechnol, Trubetskay Str 8,Bldg 2, Moscow 119991, Russia.
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JetBrains Res, Machine Learning Applicat & Deep Learning Grp, Kantemirovskaya Str 2, St Petersburg 197342, Russia Schiöth: Funktionell farmakologi (creator_code:org_t)
- 2021-10-05
- 2021
- English.
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In: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 22:19
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Abstract
Subject headings
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- 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 (Jnkl)); 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)
Keyword
- BPA
- BPA-exposure datasets
- DNA repair
- cellular junction
Publication and Content Type
- ref (subject category)
- art (subject category)
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