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
AbstractTranscriptomics 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.
|Journal series||Nanomaterials, ISSN 2079-4991, (N/A 70 pkt)|
|Keywords in English||toxicogenomics, transcriptomics, datamodelling, benchmark dose analysis; network analysis, read-across, QSAR, machine learning, deep learning, data integration|
|License||Journal (articles only); published final; ; with publication|
|Score||= 70.0, 15-04-2020, ArticleFromJournal|
|Publication indicators||: 2018 = 4.034 (2) - 2018=4.358 (5)|
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