인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In the rapidly advancing field of cancer genomics, identifying new cancer genes and understanding their molecular mechanisms are essential for advancing targeted therapies and improving patient outcomes. This study explores the capability of Graph Convolutional Networks (GCNs) for integrating complex multiomics data to uncover intricate biological relationships. However, the inherent complexity of GCNs often limits their interpretability, posing challenges for practical applications in clinical settings. To enhance explainability, we systematically compare two state-of-the-art interpretability methods: Integrated Gradients (IG) and SHapley Additive exPlanations (SHAP). We quantify model performance through various metrics, achieving an accuracy of 76% and an Area Under the ROC curve is 0.78, indicating the model’s effective identification of both overall predictions and positive instances. We analyze and compare explanations provided by IG and SHAP to gain more knowledge in the decision-making processes of GCNs. Our framework interpret the contributions of various omics features in GCN models, with the highest SHAP score observed for feature MF:UCEC and the highest IG score for KIF11. This approach identifies novel cancer genes and clarifies their molecular mechanisms, enhancing GCN interpretability. The study improves GCN accessibility in personalized medicine and contributes to understanding cancer biology.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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