首页> 美国卫生研究院文献>Bioinformatics >Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

【2h】

Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

机译：通过非参数模型中的ChIP芯片和表达数据集成来学习转录网络

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

摘要
著录项
相似文献
相关主题

摘要

>Results: We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP–chip and Expression data), an algorithm for learning a transcriptional network from ChIP–chip and expression data. The network is specified by a binary matrix of transcription factor (TF)–gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood.We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.>Availability: Our algorithm is available at >Contact: >Supplementary Information: are available at Bioinformatics online.

机译：>结果：我们开发了LeTICE（从ChIP芯片和表达数据的集成中学习转录网络），一种从ChIP芯片和表达数据中学习转录网络的算法。该网络由转录因子（TF）的二元矩阵指定-基因相互作用将基因分为模块和不参与转录调控的基因背景。我们定义网络的可能性，然后搜索使可能性最优化的网络。我们将LeTICE应用于富媒体中生长的酵母细胞的位置和表达数据，以了解特定于酵母细胞周期的转录网络。它发现了12个特定于条件的TF和15个模块，每个模块都具有与细胞周期调控的特定阶段相关的功能。>可用性：我们的算法可在>联系方式： >补充信息：可在线访问生物信息学。

著录项

期刊名称 Bioinformatics
作者
Ahrim Youn; David J. Reiss; Werner Stuetzle;
展开▼
作者单位

展开▼
年(卷),期 -1(26),15
年度 -1
页码 1879–1886
总页数 8
原文格式 PDF
正文语种
中图分类应用微生物学;生化遗传学;生化药理学;
关键词

相似文献

外文文献
中文文献
专利

1. Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model [J] . Youn Ahrim, Reiss David J., Stuetzle Werner . Bioinformatics . 2010,第15期

机译：在非参数模型中从ChIP芯片和表达数据的集成中学习转录网络
2. Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model [J] . Werner Stuetzle Bioinformatics . 2010,第15期

机译：通过非参数模型中的ChIP芯片和表达数据集成来学习转录网络
3. Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks [J] . Cooke E.J., Savage R.S., Wild D.L. Seminars in cell and developmental biology . 2009,第7期

机译：在基因调节网络推断中整合基因表达，ChIP芯片和序列数据的计算方法
4. Simple modeling expressions for substrate network of on-chip inductors [C] . Lai, I.C.H., Fujishima, . 2005

机译：片上电感器衬底网络的简单建模表达式
5. Modeling and Analysis of Multiple Alignments, ChIP-seq, and Gene Expression Data for Finding Transcription Factor Binding Sites. [D] . Chen, Gong. 2010

机译：多重比对，ChIP-seq和基因表达数据的建模和分析，用于查找转录因子结合位点。
6. Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data [O] . Xiangdong Liu, Walter J Jessen, Siva Sivaganesan, 2007

机译：通过联合建模基因表达和ChIP芯片数据来发现转录模块的贝叶斯分层模型
7. Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model [O] . Youn, Ahrim, Reiss, David J., Stuetzle, Werner 2010

机译：通过非参数模型中的ChIP芯片和表达数据集成来学习转录网络
8. Learning to Classify Map Data with Cascaded VLSI Neural Network Building Block Chips [R] . Brown, T. X., Duong, T., Eberhardt, S. P., 1993

机译：学习用级联VLsI神经网络构建块芯片对地图数据进行分类

Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

摘要

著录项

相似文献

相关主题

期刊订阅