.png){kind=icon}
返回
Clinical Outcome Prediction Using Single-Cell Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TBCAS.2016.2577641.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Single-cell technologies like flow cytometry (FCM) provide valuable biological data for knowledge discovery in complex cellular systems like tissues and organs. FCM data contains multi-dimensional information about the cellular heterogeneity of intricate cellular systems. It is possible to correlate single-cell markers with phenotypic properties of those systems. Cell population identification and clinical outcome prediction from single-cell measurements are challenging problems in the field of single cell analysis. In this paper, we propose a hybrid learning approach to predict clinical outcome using samples' single-cell FCM data. The proposed method is efficient in both i) identification of cellular clusters in each sample's FCM data and ii) predict clinical outcome (healthy versus unhealthy) for each subject. Our method is robust and the experimental results indicate promising performance.
Keyword:
Cell population
classification
clustering
random forest
single-cell
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.2
论文数:
9.7K
被引数:
2.2W
机构
引用论文
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.png)
NATURE BIOTECHNOLOGY
OAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏分享收藏分享收藏
分享收藏
查看更多内容