Biobrick chain recommendations for genetic circuit design

Biobrick chain recommendations for genetic circuit design
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用于遗传电路设计的 Biobrick 链建议

DOI:
10.1016/j.compbiomed.2017.04.019
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发表时间:
2017-07
影响因子:
7.7
通讯作者:
Gil Alterovitz
Gil Alterovitz
中科院分区:
工程技术2区
文献类型:
--
作者:
Jiaoyun Yang;Song Yu;Bowen Gong;Ning An;Gil Alterovitz

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合成生物学数据库已经收集了大量的生物砖来加速基因电路的设计。然而,选择生物砖是一项坚韧的任务。在这里,我们利用这些手动设计的电路可以提供基础知识来支持生物砖选择的事实。我们建议设计一个基于现有基因电路分析的推荐系统,它可以缩小生物砖的选择范围,并提供候选生物砖供用户选择。针对这一问题,提出了一种基于马尔可夫模型的推荐策略.此外,生物砖链推荐算法Sira提出了一个动态规划过程中的分层状态转移图,以获得前k个推荐结果。此外,一个加权滤波策略,WFSira,提出了增强Sira的性能。标准生物部件注册表的实验结果表明,Sira在生物砖推荐方面明显优于其他算法,召回率提高了约30%。它还能够提出生物砖链建议。WFSira可以进一步提高Sira的召回率,对于前5个建议,平均提高7.5%。
Synthetic biology databases have collected numerous biobricks to accelerate genetic circuit design. However, selecting biobricks is a tough task. Here, we leverage the fact that these manually designed circuits can provide underlying knowledge to support biobrick selection. We propose to design a recommendation system based on the analysis of available genetic circuits, which can narrow down the biobrick selection range and provide candidate biobricks for users to choose. A recommendation strategy based on a Markov model is established to tackle this issue. Furthermore, a biobrick chain recommendation algorithm Sira is proposed that applies a dynamic programming process on a layered state transition graph to obtain the top k recommendation results. In addition, a weighted filtering strategy, WFSira, is proposed to augment the performance of Sira. The experimental results on the Registry of Standard Biological Parts show that Sira outperforms other algorithms significantly for biobrick recommendations, with approximately 30% improvement in terms of recall rate. It is also able to make biobrick chain recommendations. WFSira can further improve the recall rate of Sira by an average of 7.5% for the top 5 recommendations.
DOI: 10.1371/journal.pone.0017005
发表时间: 2011-02-24
期刊: PloS one
影响因子: 3.7
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发表时间: 2016-02
期刊: bioRxiv
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DOI: 10.1023/a:1006544522159
发表时间: 1999-12-01
影响因子: 12
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