Topical Collection "Predictive Toxicology"
A topical collection in International Journal of Environmental Research and Public Health (ISSN 1660-4601). This collection belongs to the section "Toxicology and Public Health".
The recent advances of toxicogenomics, high-throughput screening, stem cells, and image analysis are creating unique opportunities to improve our ability to predict risk in humans and the development of predictive toxicology. These modern biotechnologies are producing big toxicological data and require advanced artificial intelligence technologies to evaluate the potential for predicting toxicity. The application of conventional machine learning algorithms, such as logical regression, decision tree, and support vector machines, have largely enhanced our capability to recover useful knowledge from the increasing volume of toxicity data. A recent study reported by researchers from John Hopkins University, demonstrated that using artificial intelligent algorithms trained on chemical-safety, big data could be more predictive and outperform expensive animals studies on some toxicities. Notably, the development of deep learning techniques, with the help of advanced computer technologies (e.g., the use of graphical processing units (GPU)) and complicated neural network algorithms, have brought about breakthroughs in computer vision and pattern recognition, image and speech recognition, drug discovery, and toxicology. In several public scientific challenges, including the Merck-sponsored Kaggle competition in 2012 and the Tox21 Data Challenge in 2015, deep learning algorithms demonstrated a superior predictive performance to convenient machine learning algorithms.
In this Topical Collection, we focus on exploring the relationship between the toxicity of xenobiotics and their chemical structures, disturbed cellular, and molecular pathways by the application of artificial intelligent methods to improve the prediction of toxicity risk. In addition, we especially encourage submissions on applying deep learning techniques to process datasets from high-dimensional gene expression, image and high-throughput screening, and chemical structures.
Dr. Minjun Chen
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 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.
- Predictive toxicology
- Artificial intelligence
- Big data
- Machine learning
- Deep learning
- High throughput screening
- Image analysis
- Chemical structure