Inference of genetic networks using random forests: Assigning different weights for gene expression data
Inference of genetic networks using random forests: Assigning different weights for gene expression data
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DOI：
10.1142/s021972001950015x
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发表时间：
2019-08-01
影响因子：
1
通讯作者：
Okada, Mariko
中科院分区：
文献类型：
--
作者：
Kimura, Shuhei;Tokuhisa, Masato;Okada, Mariko
在使用基因表达水平进行遗传网络推断时，我们认为两个彼此相似的测量值比两个彼此不同的测量值提供的信息少。例如，考虑到在时间序列实验中两个相邻时间点测量的基因表达水平通常彼此相似，我们假设时间序列实验中的每个测量值比稳态实验中的每个测量值提供的信息少。基于这一思想，我们提出了一种新的推理方法，在很大程度上依赖于信息丰富的基因表达数据。通过数值实验，我们证明了推断的遗传网络的质量略有改善，通过重加权信息基因表达数据。在这项研究中，我们开发了一种新的方法，通过修改现有的基于随机森林的推理方法，利用其分析时间序列和静态基因表达数据的能力。我们提出的想法也可以类似地应用于许多其他现有的推理方法。
In using gene expression levels for genetic network inference, we believe that two measurements that are similar to each other are less informative than two measurements that differ from each other. Given, for example, that gene expression levels measured at two adjacent time points in a time-series experiment are often similar to each other, we assume that each measurement in the time-series experiment will be less informative than each measurement in a steady-state experiment. Based on this idea, we propose a new inference method that relies heavily on informative gene expression data. Through numerical experiments, we prove that the quality of an inferred genetic network is slightly improved by heavily weighting informative gene expression data. In this study, we develop a new method by modifying the existing random-forest-based inference method to take advantage of its ability to analyze both time-series and static gene expression data. The idea we propose can be similarly applied to many of the other existing inference methods, as well.