Simple SummaryThe toxicogenomic approach has gained increased attention for understanding key molecular events within organisms to deal with exposure conditions. However, finding relevant biomarkers to predict toxicity still remains a challenging task. In this study, we investigated machine learning applications as a tool to aid toxicity prediction and gene ranking method by training eight different standard supervised machine learning classifiers with qPCR gene expression data. We have shown that the toxicogenomic approach can benefit greatly from machine learning to identify unique expression profiles and predict toxicity, as well as visualization of genes by using Shapley values.AbstractZinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness fromDaphnia magnagene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.