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Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction
Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation sta...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305019/ https://www.ncbi.nlm.nih.gov/pubmed/34299341 http://dx.doi.org/10.3390/ijms22147721 |
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author | Lee, Yeeun Nam, Seungyoon |
author_facet | Lee, Yeeun Nam, Seungyoon |
author_sort | Lee, Yeeun |
collection | PubMed |
description | Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction. |
format | Online Article Text |
id | pubmed-8305019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83050192021-07-25 Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction Lee, Yeeun Nam, Seungyoon Int J Mol Sci Article Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction. MDPI 2021-07-19 /pmc/articles/PMC8305019/ /pubmed/34299341 http://dx.doi.org/10.3390/ijms22147721 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Yeeun Nam, Seungyoon Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title | Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title_full | Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title_fullStr | Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title_full_unstemmed | Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title_short | Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction |
title_sort | performance comparisons of alexnet and googlenet in cell growth inhibition ic50 prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305019/ https://www.ncbi.nlm.nih.gov/pubmed/34299341 http://dx.doi.org/10.3390/ijms22147721 |
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