{"title":"[Regular Paper] Inference of Genetic Networks Using Random Forests: Use of Different Weights for Time-Series and Static Gene Expression Data","authors":"Shuhei Kimura, M. Tokuhisa, Mariko Okada","doi":"10.1109/BIBE.2018.00026","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2018.00026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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.