Enhanced Deep Learning Model for Personalized Cancer Treatment
Personalized medicine provides more safe and effective treatment by individualizing the choice of drug and dose based on an individual’s genetic profile. Cancer patients’ response to anti-cancer treatments (drugs) is one of the foremost challenges in personalized medicine that...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9903061/ |
| _version_ | 1828345191235321856 |
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| author | Hanan Ahmed Safwat Hamad Howida A. Shedeed Ashraf Saad Hussein |
| author_facet | Hanan Ahmed Safwat Hamad Howida A. Shedeed Ashraf Saad Hussein |
| author_sort | Hanan Ahmed |
| collection | DOAJ |
| description | Personalized medicine provides more safe and effective treatment by individualizing the choice of drug and dose based on an individual’s genetic profile. Cancer patients’ response to anti-cancer treatments (drugs) is one of the foremost challenges in personalized medicine that releases the target treatment. Both size and availability of drug sensitivity data have motivated researchers to develop Artificial Intelligence (AI), based models, for predicting drug response to advance cancer treatment. The concerned AI models include Machine Learning (ML) and the recently advanced Deep Learning (DL) based models. This paper introduces both; a data federation method and a DL-based model for predicting drug response. The fundamental goal is to generalize the predictor so it will be able to predict the response to different drugs accurately. As the data has a considerable effect on any AI model, the data federation is utilized to consolidate the data. The proposed consolidation process is carried out to make each cell line contains gene expression data, its mutation profile, and drug response data. ML models such as Support Vector Machine (SVM) and Linear Regression (LR) are used along with Principal Component Analysis (PCA) for feature reduction, and the AI models have been tested with and without data federation. The results show that data federation enhanced the accuracy and decreased the Mean Square Error (MSE) by almost 25%. The proposed DL model uses dimension reduction encoders. The encoder is a DL model that uses unsupervised learning. It is trained by integrating an encoder with a decoder to achieve equality between the input and output. The proposed model has achieved the best accuracy compared to some other recent models in terms of the Pearson correlation coefficient (PCC) as a performance measure. In addition, the results show that the Enhanced Deep Drug Response prediction (Enhanced Deep-DR) model has achieved the best PCC value even with the largest number of genes and drugs, which proves the high capacity and efficiency of the proposed model. Convolutional Neural Network (CNN) based-model is also implemented; it achieves higher accuracy in predicting the drug response than in some other DL-based models but less than the Enhanced Deep learning. The Enhanced Deep-DR achieves better accuracy within the range of 5% to 12% than other DL-models. |
| first_indexed | 2024-04-14T00:07:56Z |
| format | Article |
| id | doaj.art-a46611409002480f8ea941ffe345fda7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T00:07:56Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a46611409002480f8ea941ffe345fda72022-12-22T02:23:28ZengIEEEIEEE Access2169-35362022-01-011010605010605810.1109/ACCESS.2022.32092859903061Enhanced Deep Learning Model for Personalized Cancer TreatmentHanan Ahmed0https://orcid.org/0000-0002-1131-2590Safwat Hamad1https://orcid.org/0000-0002-1338-8724Howida A. Shedeed2https://orcid.org/0000-0003-3785-8740Ashraf Saad Hussein3Faculty of Computer and Information Sciences, Ain Shams University, Cairo, EgyptFaculty of Computer and Information Sciences, Ain Shams University, Cairo, EgyptFaculty of Computer and Information Sciences, Ain Shams University, Cairo, EgyptFaculty of Computer Science and Engineering, King Salman International University, El Tur, EgyptPersonalized medicine provides more safe and effective treatment by individualizing the choice of drug and dose based on an individual’s genetic profile. Cancer patients’ response to anti-cancer treatments (drugs) is one of the foremost challenges in personalized medicine that releases the target treatment. Both size and availability of drug sensitivity data have motivated researchers to develop Artificial Intelligence (AI), based models, for predicting drug response to advance cancer treatment. The concerned AI models include Machine Learning (ML) and the recently advanced Deep Learning (DL) based models. This paper introduces both; a data federation method and a DL-based model for predicting drug response. The fundamental goal is to generalize the predictor so it will be able to predict the response to different drugs accurately. As the data has a considerable effect on any AI model, the data federation is utilized to consolidate the data. The proposed consolidation process is carried out to make each cell line contains gene expression data, its mutation profile, and drug response data. ML models such as Support Vector Machine (SVM) and Linear Regression (LR) are used along with Principal Component Analysis (PCA) for feature reduction, and the AI models have been tested with and without data federation. The results show that data federation enhanced the accuracy and decreased the Mean Square Error (MSE) by almost 25%. The proposed DL model uses dimension reduction encoders. The encoder is a DL model that uses unsupervised learning. It is trained by integrating an encoder with a decoder to achieve equality between the input and output. The proposed model has achieved the best accuracy compared to some other recent models in terms of the Pearson correlation coefficient (PCC) as a performance measure. In addition, the results show that the Enhanced Deep Drug Response prediction (Enhanced Deep-DR) model has achieved the best PCC value even with the largest number of genes and drugs, which proves the high capacity and efficiency of the proposed model. Convolutional Neural Network (CNN) based-model is also implemented; it achieves higher accuracy in predicting the drug response than in some other DL-based models but less than the Enhanced Deep learning. The Enhanced Deep-DR achieves better accuracy within the range of 5% to 12% than other DL-models.https://ieeexplore.ieee.org/document/9903061/Artificial intelligenceartificial neural networksbiomedicalfeedforward neural networkspersonalized medicinedrug response prediction |
| spellingShingle | Hanan Ahmed Safwat Hamad Howida A. Shedeed Ashraf Saad Hussein Enhanced Deep Learning Model for Personalized Cancer Treatment IEEE Access Artificial intelligence artificial neural networks biomedical feedforward neural networks personalized medicine drug response prediction |
| title | Enhanced Deep Learning Model for Personalized Cancer Treatment |
| title_full | Enhanced Deep Learning Model for Personalized Cancer Treatment |
| title_fullStr | Enhanced Deep Learning Model for Personalized Cancer Treatment |
| title_full_unstemmed | Enhanced Deep Learning Model for Personalized Cancer Treatment |
| title_short | Enhanced Deep Learning Model for Personalized Cancer Treatment |
| title_sort | enhanced deep learning model for personalized cancer treatment |
| topic | Artificial intelligence artificial neural networks biomedical feedforward neural networks personalized medicine drug response prediction |
| url | https://ieeexplore.ieee.org/document/9903061/ |
| work_keys_str_mv | AT hananahmed enhanceddeeplearningmodelforpersonalizedcancertreatment AT safwathamad enhanceddeeplearningmodelforpersonalizedcancertreatment AT howidaashedeed enhanceddeeplearningmodelforpersonalizedcancertreatment AT ashrafsaadhussein enhanceddeeplearningmodelforpersonalizedcancertreatment |