Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mutagenicity and Carcinogenicity

Mutagenicity and Carcinogenicity

Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Exposure, Cytotoxicity and Cellular Uptake of Silver (Ag) and Gold (Au) Nanoparticles in Human Bronchial Epithelial Cells During Nanoparticle Synthesis.

Nanomaterials (Basel, Switzerland)·2026

Same author

Toxic or non-toxic: a critical assessment of the contradictory results for selected graphene family materials.

Frontiers in toxicology·2026

Same author

Expanding the applicability of a multicomponent nano-quantitative structure-property relationships approach from hard to soft nanomaterials: predicting liposome stability.

Nanoscale·2026

Same author

Modelling an alternative lipophilicity scale of bisphenols using biomimetic chromatography: Relevance to membrane-driven baseline toxicity.

ADMET & DMPK·2026

Same author

Human Health Risk Assessment During the Synthesis and Application of Engineered Nanomaterials in a Controlled Laboratory Environment.

Toxics·2026

Same author

Toward In Silico NAMs Analysis of Thyroid Disruption Leading to Developmental Neurotoxicity─A Collection of AOP-Anchored Computational Models.

Chemical research in toxicology·2026

Same journal

RETRACTED: Al-Hussain et al. Application of New Sodium Vinyl Sulfonate-co-2-Acrylamido-2-me[thylpropane Sulfonic Acid Sodium Salt-Magnetite Cryogel Nanocomposites for Fast Methylene Blue Removal from Industrial Waste Water. <i>Nanomaterials</i> 2018, <i>8</i>, 878.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Correction: Jiang et al. Methods for Obtaining One Single Larmor Frequency, Either <i>v</i><sub>1</sub> or <i>v</i><sub>2</sub>, in the Coherent Spin Dynamics of Colloidal Quantum Dots. <i>Nanomaterials</i> 2023, <i>13</i>, 2006.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Correction: Ekman et al. Synthesis, Characterization, and Adsorption Properties of Nitrogen-Doped Nanoporous Biochar: Efficient Removal of Reactive Orange 16 Dye and Colorful Effluents. <i>Nanomaterials</i> 2023, <i>13</i>, 2045.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-Based Materials and Coatings for De-Icing and Defogging of Wind Turbine Blades: Materials Basis, Structural Design, Engineering Integration, and Future Opportunities.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Influence of the Ripeness Stages of the Precursors on the Optical Characteristics of Carbon Dots Obtained from Valencia Orange Peels (<i>Citrus sinensis</i> L. Osbeck) by Hydrothermal Synthesis.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Insights into ALD Growth of Al-Based Dielectric Stack on 4H-SiC.

Nanomaterials (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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

Angela Serra^1,2, Michele Fratello^1,2, Luca Cattelani^1,2

¹Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland.

Nanomaterials (Basel, Switzerland)

|April 12, 2020

Summary

This summary is machine-generated.

This review explores data modeling in toxicogenomics (TGx), focusing on transcriptomics. Advanced artificial intelligence (AI) and modeling techniques enhance chemical safety assessments using TGx data.

Keywords:

QSAR benchmark dose analysis data integration data modelling deep learning machine learning network analysis read-across toxicogenomics transcriptomics

More Related Videos

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

Published on: January 15, 2020

Related Experiment Videos

Last Updated: Dec 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

Published on: January 15, 2020

Area of Science:

Toxicogenomics
Transcriptomics
Computational Toxicology

Background:

Transcriptomics data offers valuable insights for toxicogenomics (TGx).
Increasing omics datasets and analytical methods facilitate TGx research.
AI methods are increasingly applied to analyze large TGx datasets.

Purpose of the Study:

To review the state-of-the-art in data modeling for transcriptomics in TGx.
To highlight the application of AI and advanced modeling techniques in TGx.
To discuss the role of TGx data modeling in chemical safety assessment.

Main Methods:

Review of benchmark dose (BMD) analysis in TGx.
Examination of read-across and adverse outcome pathway (AOP) modeling.
Discussion of network-based approaches for mechanism of action (MOA) and biomarker discovery.
Overview of artificial intelligence (AI) methodologies, including deep learning (DL) and data integration.

Main Results:

Data modeling, particularly with AI, is crucial for predictive toxicology using TGx data.
BMD analysis, read-across, AOPs, and network-based approaches are key TGx modeling tools.
AI and DL methods enable the development of robust predictive classification and regression models for TGx.

Conclusions:

Modeling of TGx transcriptomics data is essential for accurate chemical safety assessment.
Advanced computational approaches significantly enhance the utility of TGx data.
This review provides a comprehensive overview of current modeling strategies in TGx.