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

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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 2020 , ' Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment ' , Nanomaterials , vol. 10 , no. 4 , 708 . https://doi.org/10.3390/nano10040708

Title: Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment
Author: Serra, Angela; Fratello, Michele; Cattelani, Luca; Liampa, Irene; Melagraki, Georgia; Kohonen, Pekka; Nymark, Penny; Federico, Antonio; Kinaret, Pia Anneli Sofia; Jagiello, Karolina; Ha, My Kieu; Choi, Jang-Sik; Sanabria, Natasha; Gulumian, Mary; Puzyn, Tomasz; Yoon, Tae-Hyun; Sarimveis, Haralambos; Grafström, Roland; Afantitis, Antreas; Greco, Dario
Contributor organization: Institute of Biotechnology
Date: 2020-04
Language: eng
Number of pages: 26
Belongs to series: Nanomaterials
ISSN: 2079-4991
DOI: https://doi.org/10.3390/nano10040708
URI: http://hdl.handle.net/10138/320333
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.
Subject: toxicogenomics
transcriptomics
data modelling
benchmark dose analysis
network analysis
read-across
QSAR
machine learning
deep learning
data integration
NONNEGATIVE MATRIX FACTORIZATION
GENE-COEXPRESSION NETWORK
FEATURE-SELECTION
EXPRESSION DATA
DRUG DISCOVERY
DOSE-RESPONSE
TOXICITY PREDICTION
VARIABLE SELECTION
CONNECTIVITY MAP
MICROARRAY DATA
1182 Biochemistry, cell and molecular biology
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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