TY - GEN
T1 - Survival prediction of lung cancer patients by integration of clinical and molecular features using machine learning
AU - Basit, Syed Abdullah
AU - Qureshi, Rizwan
AU - Shahid, Ali Raza
AU - Khan, Sheheryar
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Among all kinds of cancer, lung cancer has the greatest fatality rate. The mutation in Epidermal growth factor receptor (EGFR) is a significant cause of cancer deaths. Lung cancer is often diagnosed at advanced cancer stages. In this work, we propose a model by integrating patient's personal information and molecular features using machine learning classifiers, and molecular dynamics simulation. The clinical information is taken from various published studies, and molecular features are extracted using the drug-protein interactions and binding free energy of the drug-protein complex. The proposed model achieves good accuracy with a random forest classifier and a deep neural network. We believe that the prediction can be a promising index, and may help physicians and oncologists to develop personalized therapies for lung cancer patients.
AB - Among all kinds of cancer, lung cancer has the greatest fatality rate. The mutation in Epidermal growth factor receptor (EGFR) is a significant cause of cancer deaths. Lung cancer is often diagnosed at advanced cancer stages. In this work, we propose a model by integrating patient's personal information and molecular features using machine learning classifiers, and molecular dynamics simulation. The clinical information is taken from various published studies, and molecular features are extracted using the drug-protein interactions and binding free energy of the drug-protein complex. The proposed model achieves good accuracy with a random forest classifier and a deep neural network. We believe that the prediction can be a promising index, and may help physicians and oncologists to develop personalized therapies for lung cancer patients.
KW - Cancer
KW - Machine learning
KW - Precision medicine
KW - Survival prediction
UR - http://www.scopus.com/inward/record.url?scp=85125677379&partnerID=8YFLogxK
U2 - 10.1109/ICOSST53930.2021.9683898
DO - 10.1109/ICOSST53930.2021.9683898
M3 - Conference contribution
AN - SCOPUS:85125677379
T3 - 2021 15th International Conference on Open Source Systems and Technologies, ICOSST 2021 - Proceedings
BT - 2021 15th International Conference on Open Source Systems and Technologies, ICOSST 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Open Source Systems and Technologies, ICOSST 2021
Y2 - 15 December 2021 through 16 December 2021
ER -