Review of Personalized Cancer Treatment with Machine Learning

Ahmed, Hanan; Hamad, Safwat; Shedeed, Howida A.; Saad, Ashraf

doi:10.1109/icci54321.2022.9756124

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…The tDNN is the second best one with PCC equal= 0.8841, then consDeepSignaling with PCC equal= 0.85. As a result of the previous, the efficiency of deep learning based models gains the best results [48] over other algorithms. See Fig.…”

Section: Comparative Studymentioning

confidence: 79%

Enhanced Deep Learning Model for Personalized Cancer Treatment

et al. 2022

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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.

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Section: Comparative Studymentioning

confidence: 79%

Enhanced Deep Learning Model for Personalized Cancer Treatment

et al. 2022

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“…As stated in [5] machine learning (ML) models are used starting from 2010 to predict the probability of developing, redeveloping and, survivability of cancer. Since 2017, both ML and deep learning (DL) algorithms have been used for predicting cancer treatment responses.…”

Section: Related Workmentioning

confidence: 99%

“…Studying the genetic profiles and deciding the best treatment is a very time-consuming process and requires a huge human effort. According to [5] the use of Artificial Intelligent (traditional machine learning models) in cancer field begins in 2010 to predict the cancer development, likelihood of redevelopment after recovery, life expectancy and the survivability. In this stage only non-genetic features are use as medical records and history.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Models for Cancer Treatment Response Prediction

Ahmed

Shedeed

Hamad

et al. 2023

IJICIS

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Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor. Gene expression and mutation profiles are the most used biomarkers for cancer prediction. Machine learning and deep learning algorithms have been used to predict drug response. The recent research show that the performance of deep learning models is better than the performance of machine learning based one. In this paper, Convolutional Neural Network (CNN) models use are introduced to predict different drugs response. DeepInsight algorithm used to convert the input data to images to be more suitable as input to the CNN. Three different pretrained CNNs-models (InceptionV3, Xception, EfficientNetB7) are introduced with alternatives in their settings of the training process and modification in their architectures to be able to predict the drug response using IC50 regression values. Those models are selected due to their efficiency for ImageNet applications.the proposed modified Xception model achieves the best accuracy over the 2 others. At first, the whole data input passes through DeepInsight which converts the gene expression data and mutation data to images. Dimension reduction is then applied using the helper technique inside the DeepIsignt. Comparative analysis with other Deep models, shows that the proposed approach improve the prediction accuracy in a range between 14% and 22% as a reduction in mean squared error (MSE).

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