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

Nanomaterials

MyKieu Ha¹  Tae-Hyun Yoon¹  Jang-Sik Choi¹  Michele Fratello²  Angela Serra²  Antonio Federico²  Dario Greco²  PiaAnneli Sofia Kinaret²  Luca Cattelani²  Penny Nymark³  Pekka Kohonen³  Roland Grafström³  Georgia Melagraki⁴  Antreas Afantitis⁴  Mary Gulumian⁵  Natasha Sanabria⁵  Tomasz Puzyn⁶  Karolina Jagiello⁶  Irene Liampa⁷  Haralambos Sarimveis⁷ 

[1] Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea;Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland;Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden;Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus;National Institute for Occupational Health, Johannesburg 30333, South Africa;QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland;School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece;

关键词: toxicogenomics;    transcriptomics;    data modelling;    benchmark dose analysis;    network analysis;    read-across;   

DOI  :  10.3390/nano10040708

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown