The integration of gene expression data with other biological datasets holds promise for advancing the field of toxicology. Emerging technologies such as machine learning and artificial intelligence are being employed to analyze complex datasets, offering new insights into the mechanisms of toxicity. Personalized toxicology, which considers individual genetic variations in response to toxicants, is another exciting area of research. As these approaches evolve, they will enhance our ability to predict, assess, and mitigate the risks associated with toxic exposures.
