.png){kind=icon}
返回
Meta-analytic support vector machine for integrating multiple omics data
.png){kind=icon}
OA
AI
DOI：10.1186/s13040-017-0126-8.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Of late, high-throughput microarray and sequencing data have been extensively used to monitor biomarkers and biological processes related to many diseases. Under this circumstance, the support vector machine (SVM) has been popularly used and been successful for gene selection in many applications. Despite surpassing benefits of the SVMs, single data analysis using small- and mid-size of data inevitably runs into the problem of low reproducibility and statistical power. To address this problem, we propose a meta-analytic support vector machine (Meta-SVM) that can accommodate multiple omics data, making it possible to detect consensus genes associated with diseases across studies. Results: Experimental studies show that the Meta-SVM is superior to the existing meta-analysis method in detecting true signal genes. In real data applications, diverse omics data of breast cancer (TCGA) and mRNA expression data of lung disease (idiopathic pulmonary fibrosis; IPF) were applied. As a result, we identified gene sets consistently associated with the diseases across studies. In particular, the ascertained gene set of TCGA omics data was found to be significantly enriched in the ABC transporters pathways well known as critical for the breast cancer mechanism. Conclusion: The Meta-SVM effectively achieves the purpose of meta-analysis as jointly leveraging multiple omics data, and facilitates identifying potential biomarkers and elucidating the disease process.
Keyword:
Support vector machine
Meta-analysis
Data integration
TCGA
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:
6.1
论文数:
700
被引数:
1.5K
机构
引用论文
The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements微阵列质量控制 (MAQC) 项目显示了基因表达测量的平台间和平台内可重复性.png)
NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏Local structural change in GaCrN grown by radio frequency plasma-assisted molecular-beam epitaxy
PREAI
分享收藏分享收藏Transition dipole interaction in polypeptides: Ab initio calculation of transition dipole parametersOAAI
OAAI分享收藏A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer预测他莫昔芬治疗的淋巴结阴性乳腺癌复发的多基因检测OAAI
OAAI分享收藏分享收藏
查看更多内容