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

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 toxici...

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Detalles Bibliográficos
Autores principales: Wu, Yunyi, Wang, Guanyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6121588/
https://www.ncbi.nlm.nih.gov/pubmed/30103448
http://dx.doi.org/10.3390/ijms19082358
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author Wu, Yunyi
Wang, Guanyu
author_facet Wu, Yunyi
Wang, Guanyu
author_sort Wu, Yunyi
collection PubMed
description 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.
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spelling pubmed-61215882018-09-07 Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis Wu, Yunyi Wang, Guanyu Int J Mol Sci Review 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. MDPI 2018-08-10 /pmc/articles/PMC6121588/ /pubmed/30103448 http://dx.doi.org/10.3390/ijms19082358 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Wu, Yunyi
Wang, Guanyu
Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title_full Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title_fullStr Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title_full_unstemmed Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title_short Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
title_sort machine learning based toxicity prediction: from chemical structural description to transcriptome analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6121588/
https://www.ncbi.nlm.nih.gov/pubmed/30103448
http://dx.doi.org/10.3390/ijms19082358
work_keys_str_mv AT wuyunyi machinelearningbasedtoxicitypredictionfromchemicalstructuraldescriptiontotranscriptomeanalysis
AT wangguanyu machinelearningbasedtoxicitypredictionfromchemicalstructuraldescriptiontotranscriptomeanalysis