Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.
QSAR
benchmark dose analysis
data integration
data modelling
deep learning
machine learning
network analysis
read-across
toxicogenomics
transcriptomics
Journal
Nanomaterials (Basel, Switzerland)
ISSN: 2079-4991
Titre abrégé: Nanomaterials (Basel)
Pays: Switzerland
ID NLM: 101610216
Informations de publication
Date de publication:
08 Apr 2020
08 Apr 2020
Historique:
received:
10
03
2020
revised:
25
03
2020
accepted:
26
03
2020
entrez:
12
4
2020
pubmed:
12
4
2020
medline:
12
4
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32276469
pii: nano10040708
doi: 10.3390/nano10040708
pmc: PMC7221955
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Academy of Finland
ID : 322761
Organisme : EU H2020 NanosolveIT
ID : 814572
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