Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells

Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning...

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Autores principales:	Fazileh Esmaeili, Tahmineh Lohrasebi, Manijeh Mohammadi-Dehcheshmeh, Esmaeil Ebrahimie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	meta-analysis supervised machine learning decision tree transcription factors herbal compound Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2c32ad2bbf494f4ca8d14cf9713d5579
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spelling	oai:doaj.org-article:2c32ad2bbf494f4ca8d14cf9713d55792021-11-25T17:11:53ZEvaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells10.3390/cells101131392073-4409https://doaj.org/article/2c32ad2bbf494f4ca8d14cf9713d55792021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4409/10/11/3139https://doaj.org/toc/2073-4409Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.Fazileh EsmaeiliTahmineh LohrasebiManijeh Mohammadi-DehcheshmehEsmaeil EbrahimieMDPI AGarticlemeta-analysissupervised machine learningdecision treetranscription factorsherbal compoundBiology (General)QH301-705.5ENCells, Vol 10, Iss 3139, p 3139 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	meta-analysis supervised machine learning decision tree transcription factors herbal compound Biology (General) QH301-705.5
spellingShingle	meta-analysis supervised machine learning decision tree transcription factors herbal compound Biology (General) QH301-705.5 Fazileh Esmaeili Tahmineh Lohrasebi Manijeh Mohammadi-Dehcheshmeh Esmaeil Ebrahimie Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
description	Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.
format	article
author	Fazileh Esmaeili Tahmineh Lohrasebi Manijeh Mohammadi-Dehcheshmeh Esmaeil Ebrahimie
author_facet	Fazileh Esmaeili Tahmineh Lohrasebi Manijeh Mohammadi-Dehcheshmeh Esmaeil Ebrahimie
author_sort	Fazileh Esmaeili
title	Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
title_short	Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
title_full	Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
title_fullStr	Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
title_full_unstemmed	Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
title_sort	evaluation of the effectiveness of herbal components based on their regulatory signature on carcinogenic cancer cells
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2c32ad2bbf494f4ca8d14cf9713d5579
work_keys_str_mv	AT fazilehesmaeili evaluationoftheeffectivenessofherbalcomponentsbasedontheirregulatorysignatureoncarcinogeniccancercells AT tahminehlohrasebi evaluationoftheeffectivenessofherbalcomponentsbasedontheirregulatorysignatureoncarcinogeniccancercells AT manijehmohammadidehcheshmeh evaluationoftheeffectivenessofherbalcomponentsbasedontheirregulatorysignatureoncarcinogeniccancercells AT esmaeilebrahimie evaluationoftheeffectivenessofherbalcomponentsbasedontheirregulatorysignatureoncarcinogeniccancercells
_version_	1718412637513973760

Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells

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