[Regular Paper] Inference of Genetic Networks Using Random Forests: Use of Different Weights for Time-Series and Static Gene Expression Data
2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)(2018)
摘要
Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.
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关键词
GENIE3,genetic network inference,random forest,gene expression data
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