Guided optimization of ToxPi model weights using a semiautomated approach
The Toxicological Prioritization Index (ToxPi) is an advanced tool designed to organize and visualize complex data for decision-making. Originally developed for comparing the relative toxicity of chemicals, ToxPi combines multidimensional data into visual profiles called “slices” and produces a score for ranking different entities. Beyond chemical toxicity profiling, ToxPi has been applied in other areas, such as in the analysis of geospatial data, including county-level COVID-19 risks and sites of historical exposure to per- and polyfluoroalkyl substances (PFAS). The ToxPi scoring system depends on user-defined weights for features (data attributes), but accurately assigning these weights can be a challenge, as users often lack the tools or statistical methods to determine them properly. To address this, NIEHS researchers developed a semi-supervised method that predicts feature weights using ordinal regression (Fleming et al. 2024). This approach refines the predicted weights through a customized genetic algorithm to further optimize accuracy. A simulation study revealed that this method improves feature-weight prediction, even when only limited reference samples are available, making it effective in scenarios with sparse initial data. Researchers validated this method by building a ToxPi model using published data and comparing it to models built with expert-defined weights. The semi-supervised approach demonstrated strong performance, allowing users to generate accurate and meaningful rankings without requiring extensive prior knowledge or statistical assumptions. This enhancement to ToxPi simplifies the process of weight assignment and improves the accuracy of profiling and ranking entities, making it a valuable tool for analyzing complex datasets in toxicology, public health, and environmental science. It expands ToxPi’s utility while providing a method to guide users with minimal initial data.