Special Issue "Big Data, Machine and Deep Learning Methods for Transformative Approaches in Toxicology"

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 655

Special Issue Editor

Special Issue Information

Dear Colleagues,

Big data and artificial intelligence (AI) approaches, including machine and deep learning, are playing an increasingly important role in toxicology research. This field is still in its infancy, and more research is needed in areas such as predictive toxicology, adverse drug reactions, toxicity pathway analysis, environmental toxicology, image analysis, toxicity prediction for occupational and environmental exposure, risk assessment, and AI model explainability. Multi-omics data integration, real-time toxicity monitoring, customized toxicology, human–computer interfaces, ethics and laws, high-throughput screening, medication repurposing, and toxicity prediction across species are among the other fields of research. The purpose of these investigations is to increase the accuracy, robustness, and interpretability of AI models in order to inform risk assessments, improve decision-making in chemical control and public health, and understand the molecular causes of toxicity. Text mining of the scientific literature is one example of how big data and AI may be utilized in toxicology research to uncover new possible targets for drug development by extracting information from publications and identifying commonly cited compounds and biological processes relevant to toxicity.

Social media and digital media can also help toxicology research by collecting large amounts of data on people's interactions with toxic compounds, validating the results of AI models, identifying emerging trends, monitoring environmental exposure, engaging the public in discussions, polling public opinion, and enabling participatory toxicology approaches. However, problems of data privacy, dependability, and bias must be addressed to assure the data's credibility. These platforms can also be used in forensic toxicology to collect information about a person's drug use, create a timeline of drug use, and track a person's movements, but the challenge is to verify the information through other means and not rely on it as the sole basis for any forensic toxicology conclusions.

In the domains indicated above, this Special Issue asks for research on transformative big data, social and digital media, and AI approaches to toxicology. The current research in this context is in its infancy and requires more exploration from this multidisciplinary community.

Prof. Dr. Rashid Mehmood
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • big data in toxicology
  • machine and deep learning in toxicology
  • social media and digital media in toxicology
  • multi-omics data integration (e.g., genomics, proteomics, metabolomics)
  • multimedia (e.g., image, voice, video, natural language) analysis in toxicology
  • predictive and personalized toxicology
  • adverse drug reactions
  • data privacy, dependability, and bias in toxicology
  • interpretability and explainability of AI models in toxicology
  • ethics and regulations in toxicology

Published Papers (1 paper)

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Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses
Toxics 2023, 11(3), 287; https://doi.org/10.3390/toxics11030287 - 20 Mar 2023
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Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental [...] Read more.
Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters (Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media. Full article
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