Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington’s disease mice
作者: Lucile MégretSatish Sasidharan NairJulia DancourtJeff AaronsonJim RosinskiChristian Neri
作者单位: 1Sorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France
2CHDI Foundation, Princeton, NJ, USA
刊名: BMC Bioinformatics, 2020, Vol.21 (1), pp.623-633
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-020-3418-9
关键词: Machine learningMultidimensional dataMiRNA regulationShape analysisPredictive accuracyBiological precision
英文摘要: Abstract(#br)Background(#br)MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice. Results(#br)Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach...
全文获取路径: Springer Nature  (合作)
影响因子:3.024 (2012)