Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.
TLDR
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 subset.Abstract:
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.read more
Citations
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Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework
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TL;DR: The recent state of knowledge on nanopesticide risk assessment is analyzed, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals.
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Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook
TL;DR: The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds as discussed by the authors.
References
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Data mining: practical machine learning tools and techniques with Java implementations
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TL;DR: Elements of Sampling Theory and Methods is unique in its presentation of materials, and the book’s price is reasonable in comparison to the other four books mentioned in this area.
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