.png){kind=icon}
返回
Disease characterization using a partial correlation-based sample-specific network
.png){kind=icon}
PRE
AI
DOI：10.1093/bib/bbaa062.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN) . By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.
Keyword:
sample-specific network
driver mutation prediction
network distance
subtype identification
phenotype classification
scRNA-seq data classification
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:
7.7
论文数:
5.8K
被引数:
2.7W
机构
引用论文
Personalized characterization of diseases using sample-specific networks使用样本特定网络对疾病进行个性化表征.png)
NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells人植入前胚胎和胚胎干细胞的单细胞rna-seq谱
PREAI
分享收藏Envisioning the future of health systems: Exploratory insights from European countriesFutures
FuturesIF0
PREAI
分享收藏Identification of sample-specific regulations using integrative network level analysisBMC CANCEROAAI
BMC CANCERIF3.4
OAAI分享收藏分享收藏
查看更多内容