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Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties
Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671159/ https://www.ncbi.nlm.nih.gov/pubmed/26644976 http://dx.doi.org/10.7717/peerj.1425 |
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author | Yue, Zhenyu Zhang, Wenna Lu, Yongming Yang, Qiaoyue Ding, Qiuying Xia, Junfeng Chen, Yan |
author_facet | Yue, Zhenyu Zhang, Wenna Lu, Yongming Yang, Qiaoyue Ding, Qiuying Xia, Junfeng Chen, Yan |
author_sort | Yue, Zhenyu |
collection | PubMed |
description | Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity. |
format | Online Article Text |
id | pubmed-4671159 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46711592015-12-07 Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties Yue, Zhenyu Zhang, Wenna Lu, Yongming Yang, Qiaoyue Ding, Qiuying Xia, Junfeng Chen, Yan PeerJ Bioinformatics Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity. PeerJ Inc. 2015-11-26 /pmc/articles/PMC4671159/ /pubmed/26644976 http://dx.doi.org/10.7717/peerj.1425 Text en © 2015 Yue et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Yue, Zhenyu Zhang, Wenna Lu, Yongming Yang, Qiaoyue Ding, Qiuying Xia, Junfeng Chen, Yan Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title | Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title_full | Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title_fullStr | Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title_full_unstemmed | Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title_short | Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
title_sort | prediction of cancer cell sensitivity to natural products based on genomic and chemical properties |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671159/ https://www.ncbi.nlm.nih.gov/pubmed/26644976 http://dx.doi.org/10.7717/peerj.1425 |
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