@inproceedings{967e19a12c4045c889665fd177b3b071,
title = "Drug response prediction for lung cancer patients using biophysical simulation and machine learning",
abstract = "Lung cancer is one of the most prevalent contributors to cancer deaths worldwide. The over-expression of Epidermal growth factor receptor (EGFR) is found in about 60% of non-small cell lung cancer (NSCLC) patients. Food and Drug Administration (FDA) has approved small molecule inhibitors, targeting the kinase domain of EGFR and to stop the abnormal growth of the cancer cells. These inhibitors produce encouraging results, but the long term efficacy remains limited due to secondary point mutations. In this work, we have developed a framework, using molecular dynamics (MD) simulation and machine learning to predict the drug response in lung cancer patients and to understand the mechanism of drug resistance. The experiments on an independent cohort of 61 patients shows the effectiveness of the proposed approach.",
keywords = "Cancer, Drug Resistance, Machine Learning, Molecular Dynamics Simulation, Oncology, Precision Medicine",
author = "Rizwan Qureshi and Tanvir Alam and Jia Wu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",
year = "2022",
doi = "10.1109/BIBM55620.2022.9995622",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3867--3869",
editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, {Ananda M.} and Jin Liu",
booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",
address = "United States",
}