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

该提案将使用2009年美国复苏和再投资法案（公法111-5）提供的资金进行奖励。通过这一早期概念的探索性研究（EAGER）赠款提案，该团队旨在证明BOINC可以成为一个有效的平台，适用于广泛的应用程序，采用基于吉布斯抽样的概率推理。具体来说，该团队希望（i）提供一个架构，吉布斯采样应用程序可以利用BOINC和（ii）通过转换系统发育吉布斯质心采样器，利用BOINC的吉布斯采样接口，这是一个16年建立的分子生物学算法的最强大版本，但需要提高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.