Construction of a deep learning model for predicting cancer prognosis using multi-omics data = 멀티오믹스 데이터를 이용한 암 예후예측 딥러닝 모델 개발

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Cancer is one of the significant causes of death globally. Therefore, early and precise detection and proper treatment decision of cancer can considerably reduce the risk of the disease in clinic practice. As many genomic data getting digitalized and become more easily accessible, deep learning is developed into a useful tool for predictive and prognostic models. Through this research, multi-omics data of liver cancer patients including seven molecular features such as expression data is systematically collected and organized as a database. Using this dataset, the prognostic model with convolutional neural network (CNN) is constructed and trained. Compared to previous prognostic models, such as linear cox, random survival forest and glmnet, the CNN prognostic model shows outstanding and robust prediction accuracy. Multi-omics data can preserve various causes of malfunction in genes. By computing feature importance of each gene’s molecular features, certain change in certain gene that affects patients’ prognosis can be discovered. These prognostic markers help to propose appropriate approach of treatment to cancer patients.
Advisors
Choi, Jung Kyoonresearcher최정균researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.8,[vi, 31 p. :]

Keywords

deep learning▼aprognostic model▼aconvolutional neural network▼amulti-omics▼afeature importance▼aliver cancer; 딥러닝▼a예후예측 모델▼a합성곱 신경망▼a멀티오믹스▼a특성 중요도▼a간암

URI
http://hdl.handle.net/10203/284939
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925098&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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