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Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

1
Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland
2
BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
3
School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
4
Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus
5
Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
6
Division of Toxicology, Misvik Biology, 20520 Turku, Finland
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Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
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University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
10
Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea
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Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
12
Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
13
National Institute for Occupational Health, Johannesburg 30333, South Africa
14
Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
*
Author to whom correspondence should be addressed.
Nanomaterials 2020, 10(4), 708; https://doi.org/10.3390/nano10040708
Received: 10 March 2020 / Revised: 25 March 2020 / Accepted: 26 March 2020 / Published: 8 April 2020
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics. View Full-Text
Keywords: toxicogenomics; transcriptomics; data modelling; benchmark dose analysis; network analysis; read-across; QSAR; machine learning; deep learning; data integration toxicogenomics; transcriptomics; data modelling; benchmark dose analysis; network analysis; read-across; QSAR; machine learning; deep learning; data integration
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MDPI and ACS Style

Serra, A.; Fratello, M.; Cattelani, L.; Liampa, I.; Melagraki, G.; Kohonen, P.; Nymark, P.; Federico, A.; Kinaret, P.A.S.; Jagiello, K.; Ha, M.K.; Choi, J.-S.; Sanabria, N.; Gulumian, M.; Puzyn, T.; Yoon, T.-H.; Sarimveis, H.; Grafström, R.; Afantitis, A.; Greco, D. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. Nanomaterials 2020, 10, 708.

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