A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

Background: Bioactivity 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 highdimensional 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 chemicaltoxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods. Results: The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Na

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Richard Judson, Fathi Elloumi, R. Woodrow Setzer, Zhen Li, Imran Shah
• Keywords: BMCBI 2008, Feature Selection, ML Methods, Vitro Assay