PO.BCS02.06 · 生物信息与计算
Machine Learning Approaches for Cancer Prediction
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- 4207 4207 通过多模态协同训练与可扩展的多组学编码器进行组学感知的图块聚合，以跨肿瘤生物标志物面板实现切片级预测 Omics-aware patch aggregation via multimodal co-training with a scalable multi-omics encoder for slide-level prediction across an oncology biomarker panel Hwanil Choi, Tae Hyun Hwang, Soonyoung Lee, Jongseong Jang
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- 4209 4209 一种整合分期、组织学、分级和治疗以预测唾液腺癌死亡率的多变量机器学习模型：一项基于 SEER 2010-2021 人群的研究 A multi-variable machine learning model integrating stage, histology, grade, and treatment to predict mortality in salivary gland cancer: A SEER 2010-2021 population-based study Chiugo Okoye, Chinemerem Martlin Emeasoba, Chidi Obialo-Ibeawuchi, Olanipekun Ntukidem, Oboseh John Ogedegbe, Uchenna Amaechi
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- 4212 4212 基线外周血 scRNA-seq AI 估计器框架通过分子基础模型和细胞到患者学习预测实体瘤反应和不良事件 Baseline peripheral blood scRNA-seq AI estimator framework predicts solid-tumor response and adverse events via molecular foundation models and cell-to-patient learning Pablo Moreno, Marta Milo, Ricardo Miragaia, Alex Proutski, Virginia Savova, Ikbel Achour
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- 4221 4221 CAPTYN：一个预测阿替利珠单抗-贝伐珠单抗治疗肝细胞癌临床获益的六变量机器学习模型——在IMbrave150中的开发与外部验证 CAPTYN, a six-variable machine-learning model predicting clinical benefit of atezolizumab-bevacizumab in hepatocellular carcinoma: Development and external validation in IMbrave150 Gae Hoon Jo, Sohyun Hwang, Bernhard Scheiner, Won Suk Lee, Beodeul Kang, Jung Sun Kim, Ho Yeong Lim, Chansik An, Dong Yun Kim, Inyoung Kim, Dong-hyuk Heo, Matthias Pinter, Beom Kyung Kim, Chan Kim, Hong Jae Chon
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- 4224 4224 基于人工智能对可穿戴设备衍生生物特征的分析以刻画实体器官癌症化疗的生理反应 Artificial intelligence-derived analysis of wearables-derived biometrics to characterize physiologic response to chemotherapy in solid organ cancers Alexander Zhu, Lizi Zhang, Yuhang Zhang, Alexandra Potter, Bryan Rettner, Alisha Keshwani, Alina Keshwani, Nicole Hu, Anton Melki, Zhengyu Fang, Meghan McCarthy, Jacob Baird, Aubrey Pope, Samuel Schwartz, Quiana Guo, Michael Lanuti, Xiao Li, Chi-Fu Jeffrey Yang
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