从组织学图像中对基因表达的数字分析,以线性化的注意力
Marija Pizurica1,2, Yuanning Zheng1, Francisco Carrillo-Perez1
1Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, 94305, USA.
Nature communications
|November 14, 2024
概括
SEQUOIA是一种新的深度学习模型,可以从病理学幻灯片中预测癌症基因表达. 这种方法有助于理解癌症和个性化患者治疗.
科学领域:
- 计算生物学是一种计算生物学.
- 数字病理学数字病理学
- 在瘤学瘤学.
背景情况:
- 癌症的异质性需要基因分析,这通常是昂贵的.
- 深度学习模型可以从整个幻灯片图像 (WSIs) 预测基因变化.
- 由于复杂性和有限的数据,变压器模型在WSI分析中面临挑战.
研究的目的:
- 介绍SEQUOIA,一种线性化变压器模型,用于从WSIs预测癌症转录组概况.
- 评估SEQUOIA在不同癌症类型和独立数据集中的表现.
- 为了证明SEQUOIA在癌症研究和临床应用中的实用性.
主要方法:
- 开发SEQUOIA,一个线性变压器模型,利用16种癌症类型的7584个瘤样本.
- 在两个独立的队列 (1368个瘤) 上验证模型概括.
- 预测基因与癌症过程的关联和临床相关性的分析.
主要成果:
- SEQUOIA准确地预测了来自WSI的癌症转录组概况.
- 预测的基因与关键的癌症途径 (炎症,细胞循环,新陈代谢) 相相关.
- 在分层乳腺癌复发风险和解决空间基因表达的证明价值.
结论:
- SEQUOIA有效地解读了WSIs中的临床相关信息.
- 该模型为传统的基因分析提供了成本效益高的替代方案.
- 通过WSI分析,为个性化癌症管理开辟了新的可能性.
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