通过残余值深度矩阵因数分解,大规模获取信息和纠正噪音制药基因组数据集
Zhiyue Tom Hu1, Yaodong Yu2, Ruoqiao Chen3
1Division of Biostatistics, University of California Berkeley, Berkeley, 94720, U.S.A.
bioRxiv : the preprint server for biology
|December 18, 2023
概括
药物基因组学研究面临着杂的药物敏感性数据. 一种新的深度学习方法,剩余值深度矩阵因子化 (RT-DMF) 纠正了这种噪音,并将缺失的值归因为更好的药物生物标志物发现.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 药物基因组学研究对于精准医学至关重要,它利用来自癌细胞系 (CCL) 的大规模药物敏感性数据集.
- 现有的药物敏感性数据集存在大量的噪音和不一致性,阻碍了准确的药物生物标志物发现和下游分析.
- 高通量实验产生了大量的数据,需要强大的降噪和数据归算方法.
结论:
- 在药物基因组学中,RT-DMF为解决数据质量问题提供了强大的解决方案.
- 该框架提高了药物敏感性数据集的可靠性,为更准确的药物生物标志物发现铺平了道路.
- 开源软件包的可用性促进了更广泛的采用,并进一步研究精密医学.
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