Abstract
BackgroundBioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.ResultsThe classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated in vitro assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.ConclusionWe have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.
Highlights
Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can reduce the need for animal testing
We evaluated the performance of different machine learning (ML) methods on simulated data sets generated by a biologically motivated analytic model
This study examined Support Vector Machines (SVM), K-Nearest Neighbors (KNN) (n = 3) and Linear Discriminant Analysis (LDA) as classification algorithms
Summary
Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can reduce the need for animal testing. The U.S EPA is carrying out one such large screening and prioritization experiment, called ToxCast, whose goal is to develop predictive signatures or classifiers that can accurately predict whether a given chemical will or will not cause particular toxicities [4]. This program is investigating a variety of chemically-induced toxicity endpoints including developmental and reproductive toxicity, neurotoxicity and cancer. The initial training set being used comes from a collection of ~300 pesticide active ingredients for which complete rodent toxicology profiles have been compiled This set of chemicals will be tested in several hundred in vitro assays
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