互补性(分子生物学)
计算机科学
数据类型
计算生物学
数据挖掘
传感器融合
相似性(几何)
生物学数据
人工智能
生物信息学
生物
遗传学
图像(数学)
程序设计语言
作者
Bo Wang,Aziz M. Mezlini,Feyyaz Demir,Marc Fiume,Zhuowen Tu,Michael Brudno,Benjamin Haibe‐Kains,Anna Goldenberg
出处
期刊:Nature Methods
[Springer Nature]
日期:2014-01-26
卷期号:11 (3): 333-337
被引量:1525
摘要
Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.
科研通智能强力驱动
Strongly Powered by AbleSci AI