.png){kind=icon}
返回
PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia
.png){kind=icon}
OA
AI
DOI：10.1186/s13040-018-0175-7.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. Results: We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancel types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. Conclusions: The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.
Keyword:
Gene expression
Unsupervised feature construction
Crosstalk
Pathway interactions
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:
699
被引数:
1.5K
机构
引用论文
Design of broadband metamaterial absorber utilized by flower-shaped unit loaded with lumped-resistor
OAAI
OAAI分享收藏ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions.png)
MSYSTEMSOAAI
MSYSTEMSIF4.6
OAAI分享收藏Wnt/β-Catenin Signaling: Components, Mechanisms, and DiseasesWnt/β-catenin信号: 成分、机制和疾病DEVELOPMENTAL CELLOAAI
DEVELOPMENTAL CELLIF8.7
OAAI分享收藏分享收藏Subsystem identification through dimensionality reduction of large-scale gene expression dataGENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
OAAI分享收藏The DREAM complex: master coordinator of cell cycle-dependent gene expressionNATURE REVIEWS CANCEROAAI
NATURE REVIEWS CANCERIF66.8
OAAI分享收藏
查看更多内容