2020
DOI: 10.1038/s41598-020-66481-0
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Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

Abstract: During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alternative to animal models for assessing drug toxicity, thus reducing expensive and invasive animal testing during clinical trials, for drugs that are most likely to fail safety tests. Here we present a machine learning model to predict kidney dysfunction, as a prox… Show more

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Cited by 21 publications
(11 citation statements)
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“…Thus, a comprehensive method is needed that will not only determine drug synergy but also incorporate the adverse effect of drug combinations. Knowledge of safe and unsafe combinations of drugs was used to build a linear regression prediction model [152] , [153] , [154] . However, the model did not incorporate any biological data to elucidate patient-specific side effects.…”
Section: Limitations In the Development Of Clinically Relevant Predictive Modelsmentioning
confidence: 99%
“…Thus, a comprehensive method is needed that will not only determine drug synergy but also incorporate the adverse effect of drug combinations. Knowledge of safe and unsafe combinations of drugs was used to build a linear regression prediction model [152] , [153] , [154] . However, the model did not incorporate any biological data to elucidate patient-specific side effects.…”
Section: Limitations In the Development Of Clinically Relevant Predictive Modelsmentioning
confidence: 99%
“…Another example is empirical scoring functions, which predict the binding affinity of ligands in docking simulations by adjusting experimentally calculated parameters ( Ashtawy and Mahapatra, 2018 , Guedes et al, 2014 ). Besides the applications mentioned above, ML can also be used to estimate an array of relevant quantitative parameters in drug discovery, including solubility, toxicity prediction, and plasma membrane permeability ( Boobier et al, 2020 , Feinberg et al, 2018 , Gardiner et al, 2020 , Mayr et al, 2016 ).…”
Section: Artificial Intelligence In Drug Discoverymentioning
confidence: 99%
“…Explainable AI was used to rank and select omic features as suggested by [75,76] and we investigated the explanations of the predictions for the DNA sequence-based ML model. Firstly, based on the best LightGBM model for the Arabidopsis Col-0 dataset from [8] on which the model was trained (i.e.…”
Section: Binary Classification: Model Explanationmentioning
confidence: 99%