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DIGITAL.CSIC
Dataset . 2023 . Peer-reviewed
Data sources: DIGITAL.CSIC
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Supplementary Data Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Authors: Diéguez, Karel; Nachimba-Mayanchi, Manuel Mesias; Puris, Amilkar; Torres, Roldán; González-Díaz, Humberto;

Supplementary Data Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Abstract

13 pages. -- Table S1. Division of data for 289 pesticides against A. bahia for mortality at 96 h (mean lethal concentration, (LC50). -- Fig. S1. Linear Backward Selection Regression results for variable selection. -- Fig. S2. Correlation heat maps of selected descriptors. -- Fig. S3. Density and frequency of pLC50 (R package). -- Fig. S4. Density line (A), histogram (B) and scatter plot (C) of the molecular descriptors. (R package). -- Fig. S5. Observed values versus predicted values for the training set. -- Fig. S6. Observed values vs. predicted values for prediction set. -- Table S2. Symbols, definitions and sign value of molecular descriptors in the QSTR-MLR model. -- Table S3. RPTree. Training Sets.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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