Art

J-GLOBAL ID：202102283004597921   Reference number：21A0084022

Inference of Genetic Networks From Time-Series and Static Gene Expression Data: Combining a Random-Forest-Based Inference Method With Feature Selection Methods

時系列および静的遺伝子発現データからの遺伝的ネットワークの推論:ランダムフォレストベース推論法と特徴選択法の結合【JST・京大機械翻訳】

Author (4)： Kimura Shuhei (Faculty of Engineering, Tottori University, Tottori, Japan) ,  Fukutomi Ryo (Graduate School of Sustainability Science, Tottori University, Tottori, Japan) ,  Tokuhisa Masato (Faculty of Engineering, Tottori University, Tottori, Japan) ,  Okada Mariko (Laboratory of Cell Systems, Institute of Protein Research, Osaka University, Osaka, Japan)
Material： Frontiers in Genetics (Web)
Volume： 11  Page： 595912  Publication year： 2020 
JST Material Number： U7071A  ISSN： 1664-8021  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Several researchers have focus...

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Thesaurus term： reliability (property) ,  *gene ,  *feature selection ,  grading (ranking) ,  *gene expression ,  numerical experiment ,  calculation cost ,  *gene expression data ,  *molecular genetic phenomenon
Semi thesaurus term： *ランダムフォレスト , 【Automatic Indexing@JST】

Author keywords (5)： FANTOM5 ,  gene expression ,  feature selection ,  random forest ,  genetic network inference

JST classification (4)： Molecular and genetic information processing  (JE15040B) ,  Gene expression  (EC02040Q) ,  Research methods in genetics  (EC01020N) ,  Artificial intelligence  (JE08000Z)

Reference (28)：

• BreimanL. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324
• CaiJ.KuoJ.WangS.YangS. (2018). Feature selection in machine learning: a new perspective. Neurocomputing 300, 70-79. doi: 10.1016/j.neucom.2017.11.077
• ChouI. C.VoitE. O. (2009). Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219, 57-83. doi: 10.1016/j.mbs.2009.03.00219327372
• ClevelandW. S. (1979). Robust locally weight regression and smoothing scatterplots. J. Am. Stat. Assoc. 79, 829-836. doi: 10.1080/01621459.1979.10481038
• de Matos SimoesR.Emmert-StreibF. (2012). Bagging statistical network inference from large-scale gene expression data. PLoS ONE 7:e33624. doi: 10.1371/journal.pone.003362422479422

Terms in the title (6)： 時系列 ,  遺伝子発現データ ,  遺伝的ネットワーク ,  推論 ,  ランダムフォレスト ,  特徴選択