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超越分子生物学–基因调控网络推理方法在生态学中的应用
Beyond Molecular Biology – Applying Gene Regulation Network Inference Methods in Ecology |
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| 课程网址: |
http://videolectures.net/licsb09_dondelinger_bmb/
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| 主讲教师: |
Frank Dondelinger
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| 开课单位: |
苏格兰生物数学统计中心
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| 开课时间: |
2009-04-16
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| 课程语种: |
英语
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| 中文简介: |
利用基因表达数据重构基因调控网络是分子生物学中的一项重要工作，目前已有多种网络推理方法。在生态学中，物种相互作用网络有着相似的目的，因为它们显示了不同物种之间的相互关系。我们研究了应用基因调控网络的方法从物种丰度数据重建物种相互作用网络的可能性。我们使用Lotka-Volterra模式的模拟模型生成基于物种交互网络的合成数据，然后尝试使用贝叶斯网络、Lasso（最小绝对收缩和选择算子）和SBR（稀疏贝叶斯回归）从这些数据重建原始网络。我们还扩展了这些方法来处理空间自相关问题。实验表明，在保持低假阳性率的同时，我们可以恢复多种物种间的相互作用。我们比较了不同的方法，发现Lasso和Bayesian网络的性能最好。
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| 课程简介: |
Reconstructing gene regulation networks from gene expression data is an important task in molecular biology for which various network inference methods have been developed. In ecology, species interaction networks serve a similar purpose, in that they show how different species relate to each other. We have investigated the possibility of applying the methods that were developed for gene regulation networks to reconstruct species interaction networks from species abundance data. We used a Lotka-Volterra style simulation model to produce synthetic data based on species interaction networks, and then tried to reconstruct the original network from this data using Bayesian networks, LASSO (Least Absolute Shrinkage and Selection Operator) and SBR (Sparse Bayesian Regression). We also developed extensions to these methods for dealing with the problem of spatial autocorrelation. Our experiments showed that we can retrieve many species interactions, while keeping the false positive rate low. We compared the different methods, and found that LASSO and Bayesian networks perform best.
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| 关 键 词: |
基因调控网络; 分子生物学; 网络推理方法; 贝叶斯网络
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-22：chenxin
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| 阅读次数: |
43
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