ReviewNanomaterials (Basel, Switzerland)2020
Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.
Review in Nanomaterials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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Who cites it
25 citing papers in PubMed.
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- The Application of Omics Technologies in Type II Diabetes Mellitus Research.Current diabetes reviews · 2026Review
- EcoToxChip provides insights into pathway perturbation upon chlorantraniliprole exposure to larval fathead minnow.Aquatic toxicology (Amsterdam, Netherlands) · 2025Article
- Review
- CHIASMA: Advancing chemicals and materials safety and sustainability assessments through innovative integration of in vitro and in silico (new approach) methodologies.Computational and structural biotechnology journal · 2025Article
- Article
- Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments.Toxicology in vitro : an international journal published in association with BIBRA · 2024Article
- A curated gene and biological system annotation of adverse outcome pathways related to human health.Scientific data · 2023Article
- Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools.Journal of cheminformatics · 2023Article
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction.Frontiers in toxicology · 2023Article
- Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives.Nanomaterials (Basel, Switzerland) · 2022Review
- Biomarkers of nanomaterials hazard from multi-layer data.Nature communications · 2022Article
- Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration.Nanomaterials (Basel, Switzerland) · 2022Article
- Nextcast: A software suite to analyse and model toxicogenomics data.Computational and structural biotechnology journal · 2022Article
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation.Archives of toxicology · 2022Article
- Prospects and challenges for FAIR toxicogenomics data.Nature nanotechnology · 2022Article
Corrections and comments
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Authors and funding
20 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.