DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최정균 | - |
dc.contributor.author | Kim, So Yeon | - |
dc.contributor.author | 김소연 | - |
dc.date.accessioned | 2024-07-25T19:30:59Z | - |
dc.date.available | 2024-07-25T19:30:59Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045789&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320601 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2023.8,[iii, 48 p. :] | - |
dc.description.abstract | T-cells and B-cells are the major components regarding tumor-specific immune activation. Complementarity determining region 3 (CDR3) immune receptor repertoires constructed from activated immune cells represent the unique cancer status in individuals. However, an approach based on immunological characteristics has yet to be widely used in the diagnosis of multiple cancers, especially in the liquid biopsy field. Hence, we apply a deep neural network model reflecting 124 cancer-specific immunological features from more than 5,000 tumors and 4,000 normal tissue-derived transcriptomes. The model successfully distinguishes 530 blood samples of various cancer types from 701 normal blood samples resulting in a ROC-AUC of 0.93. Interpretation of the model unveils the significance of features related to the B-cell receptor repertoire. Our research highlights the application of immune-derived features in a noninvasive, blood-based multiple cancer prediction. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 액체 생검▼a면역 수용체 레퍼토리▼a전사체▼a심층 신경망▼a암 예측▼aT 세포▼aB 세포 | - |
dc.subject | liquid biopsy▼aimmune receptor repertoire▼atranscriptome▼adeep neural network▼acancer prediction▼aT-cell▼aB-cell | - |
dc.title | Deep learning model for immune repertoire-based cancer prediction | - |
dc.title.alternative | 면역학적 특성에 기반한 암 진단 딥러닝 모델 개발 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Choi, Jung Kyoon | - |
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