Transcriptomics in toxicogenomics, Part III: Data modelling for risk assessment

Angela Serra , Michele Fratello , Luca Cattelani , Irene Liampa , Georgia Melagraki , Pekka Kohonen , Penny Nymark , Antonio Federico , Pia Anneli Sofia Kinaret , Karolina Jagiełło , My Kieu Ha , Jang-Sik Choi , Natasha Sanabria , Mary Gulumian , Tomasz Puzyn , Tae-Hyun Yoon , Haralambos Sarimveis , Roland Grafström , Antreas Afantitis , Dario Greco

Abstract

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.
Author Angela Serra
Angela Serra,,
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, Michele Fratello
Michele Fratello,,
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, Luca Cattelani
Luca Cattelani,,
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, Irene Liampa
Irene Liampa,,
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, Georgia Melagraki
Georgia Melagraki,,
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, Pekka Kohonen
Pekka Kohonen,,
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, Penny Nymark
Penny Nymark,,
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, Antonio Federico
Antonio Federico,,
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, Pia Anneli Sofia Kinaret
Pia Anneli Sofia Kinaret,,
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, Karolina Jagiełło (FCh / DEChR / LECh)
Karolina Jagiełło,,
- Laboratory of Environmental Chemometrics
et al.`
Journal seriesNanomaterials, ISSN 2079-4991, (N/A 70 pkt)
Issue year2020
Vol10
No4
Pages1-26
Article number708
Keywords in Englishtoxicogenomics, transcriptomics, datamodelling, benchmark dose analysis; network analysis, read-across, QSAR, machine learning, deep learning, data integration
DOIDOI:10.3390/nano10040708
URL https://doi.org/10.3390/nano10040708
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); with publication
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 15-04-2020, ArticleFromJournal
Publication indicators WoS Impact Factor: 2018 = 4.034 (2) - 2018=4.358 (5)
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