Open Access

ARTICLE

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

by Mehdi Hassan^1,*, Safdar Ali², Muhammad Sanaullah³, Khuram Shahzad⁴, Sadaf Mushtaq^5,6, Rashda Abbasi⁶, Zulqurnain Ali⁴, Hani Alquhayz⁷

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Directorate General National Repository, Islamabad, 44000, Pakistan
3 Department of Computer Science, Bahauddin Zakariya University, Multan, 60000, Pakistan
4 Department of Physics, Air University, Islamabad, 44000, Pakistan
5 Department of Biotechnology, Quaid-i-Azam University Islamabad, 44000, Pakistan
6 Institute of Biomedical and Genetic Engineering, Islamabad, 44000, Pakistan
7 Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia

* Corresponding Author: Mehdi Hassan. Email:

(This article belongs to the Special Issue: Role of Machine Learning and Evolutionary Algorithms for Cancer Detection and Prediction)

Computers, Materials & Continua 2022, 70(2), 2743-2760. https://doi.org/10.32604/cmc.2022.020055

Received 07 May 2021; Accepted 11 June 2021; Issue published 27 September 2021

Abstract

Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept. Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells. Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data. The proposed technique is validated on acquired 203 fluorescent microscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate (CFO@BTO) magnetoelectric nanoparticles in vitro. The developed approach achieved high prediction with accuracy of 97.5% and sensitivity of 100% and outperformed other approaches. The high performance reveals the effectiveness of the approach. It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung, brain tumor and breast cancer.

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