English(EN) SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

新的SHIFT模型可从不完整的基因组数据中预测生存期

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-10 04:00

研究人员开发了一种新颖的、可感知缺失的生存模型SHIFT，旨在从不完整和异构的基因组数据中预测患者生存期。与排除或填补缺失数据的方法不同，SHIFT通过掩码自注意力机制和特征可用性掩码直接利用观察到的输入。该模型在胶质母细胞瘤和肺鳞状细胞癌上进行了评估，即使在存在显著的跨队列面板不匹配的情况下，也表现出强大的泛化能力，并与标准的生存基线相比具有优势。 AI

影响该模型有望提高多中心精准肿瘤学研究中基因组生存期预测的准确性和适用性。

排序理由该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya, Benedikt Wiestler, Jana Lipkova · 2026-07-10 04:00

SHIFT：来自不完整和异构基因组数据的生存预测

arXiv:2607.07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment. Existing approaches to this challenge typically restrict an…

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SHIFT：来自不完整和异构基因组数据的生存预测

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