跨细胞:通过深度转移学习对基因组景观和细胞反应的化特征
Shan-Ju Yeh1, Shreya Paithankar1, Ruoqiao Chen2
1Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA.
Genomics, proteomics & bioinformatics
|September 6, 2024
概括
这项研究介绍了TransCell,一个深度转移学习框架,可以从基因表达数据准确预测分子特征和细胞反应. 转细胞增强了药物敏感性预测,并识别了潜在的癌症细胞系药物重定位候选人.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 细胞系的全面分子表征是资源密集的,特别是对于代表性不足的群体.
- 对各种分子测量的基因表达的预测能力需要系统的调查.
研究的目的:
- 开发和评估一个深度转移学习框架 (TransCell) 来预测基因表达的分子特征和细胞反应.
- 系统地评估基因表达在各种分子测量中的预测能力.
主要方法:
- 实施了两步深度转移学习框架 (TransCell).
- 利用来自泛癌瘤样本的知识来训练模型.
- 评估了用于预测代谢物,基因效应得分,药物敏感性,突变,拷贝数变化和蛋白质表达的机器学习方法.
主要成果:
- 在预测代谢物,基因效应得分和药物敏感性方面,TransCell表现出卓越的性能.
- 在药物敏感性预测和基因效应得分的0.7相关性方面取得了超过50%的改善.
- 确定了儿童癌症细胞系的潜在药物重新定位候选者,并揭示了黑色素瘤中BRAF抵抗机制.
结论:
- 基因表达是多种分子和细胞反应类型的强大预测因素.
- TransCell提供了一种有价值的工具,可以使用最小的资源进行全面的细胞系表征.
- 有一个网站可以从基因表达特征预测352,000个基因组和细胞反应特征.
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