Data Mining Practical Machine Learning Tools and Techniques

https://research.birmingham.ac.uk/portal/files/43085145/watermark.pdf

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In silico toxicology protocols.

Complex mixtures of dissolved pesticides show potential aquatic toxicity in a synoptic study of Midwestern U.S. streams

Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug’s preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

https://www.mdpi.com/1422-0067/19/8/2358/pdf

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development.

The ability to estimate aquatic toxicity is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based toxicity models yield the most toxicologically meaningful and, theoretically, the most accurate results. In this study, a two-step prediction methodology was developed to estimate acute aquatic toxicity from molecular structure. In the first step, one-against-the-rest linear discriminant analysis (LDA) models were used to predict the MOA. The LDA models were able to predict the MOA with 85.8–88.8% accuracy for broad and specific MOAs, respectively. In the second step, a multiple linear regression (MLR) model corresponding to the predicted MOA was used to predict the acute aquatic toxicity value. The MOA-based approach was found to yield similar external prediction accuracy (r2 = 0.529–0.632) to a single global MLR model (r2 = 0.551–0.562) fit to the entire training set. Overall, the global hierarchical clustering approach yie...

Comparison of global and mode of action-based models for aquatic toxicity

Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.

C.R. Lilavois

Papers

MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development.

Comparison of global and mode of action-based models for aquatic toxicity

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.