Citizens Science: Enabling Computational Probabilistic Methods for Organism's Transcriptional Regulatory Network Using Voluntary Computing Platforms

这项提案将使用2009年《美国复苏和再投资法案》(公法111-5)提供的资金授予。通过这项早期概念探索性研究补助金(AGER)提案，团队旨在证明BOINC可以成为一个有效的平台，适用于采用基于吉布斯抽样的概率推理的广泛类别的应用程序。具体地说，该团队希望(I)提供一个架构，使Gibbs采样应用程序可以利用BOINC，以及(Ii)通过转换系统发育Gibbs质心采样器来利用BOINC的Gibbs采样接口，Gibbs质心采样器是已建立了16年的分子生物学算法的最强大版本，该算法仍需改进以解决基因组规模序列数据集的基因调控问题。

This proposal will be awarded using funds made available by the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).With this EArly-concept Grants for Exploratory Research (EAGER)proposal, the team aims to demonstrate that BOINC can be an efficient platform for a wide class of applications employing a probabilistic inference based on Gibbs sampling. Specifically, the team expects to (i ) provide an architecture by which Gibbs sampling applications can exploitBOINC and (ii ) take advantage of the Gibbs sampling interface for BOINC by converting the Phylogenetic Gibbs Centroid Sampler, the most powerful version of a 16-year-established molecularbiology algorithm that yet needs a boost to solve gene regulatory problems with genome-scale sequence data sets.