Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

作     者：Mehdi Hassan Safdar Ali Muhammad Sanaullah Khuram Shahzad Sadaf Mushtaq Rashda Abbasi Zulqurnain Ali Hani Alquhayz 

作者机构：Department of Computer ScienceAir UniversityIslamabad44000Pakistan Directorate General National RepositoryIslamabad44000Pakistan Department of Computer ScienceBahauddin Zakariya UniversityMultan60000Pakistan Department of PhysicsAir UniversityIslamabad44000Pakistan Department of BiotechnologyQuaid-i-Azam University Islamabad44000Pakistan Institute of Biomedical and Genetic EngineeringIslamabad44000Pakistan Department of Computer Science and InformationCollege of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：2743-2760页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No.R-2021-152 

主　　题：Drug delivery in vitro transfer learning microscopic images deep learning 

摘      要：Cancer is the second deadliest human disease worldwide with high mortality *** and treatment of this disease requires precise and automatic assessment of effective drug response and control *** 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 *** hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our *** models are developed by modifying ResNet101 and exploiting the transfer learning *** three layers of ResNet101 are re-trained for the identification of drug treated cancer *** learning approach in an appropriate choice especially when there is limited amount of annotated *** proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in *** developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other *** high performance reveals the effectiveness of the *** is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.