数学与计算机科学 |
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基于高斯混合模型的肿瘤纯度估计 |
闫占正, 李玉双 |
燕山大学 理学院,河北 秦皇岛 066004 |
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Tumor purity estimation based on Gaussian mixture model |
YAN Zhanzheng, LI Yushuang |
School of Science, Yanshan University, Qinhuangdao 066004, Hebei Province, China |
1 OLSHENA B, BENGTSSONH, NEUVIALP, et al. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation[J]. Bioinformatics, 2011, 27(15): 2038-2046.DOI:10.1093/bioinformatics/btr329 2 CARTERS L, CIBULSKISK, HELMANE, et al. Absolute quantification of somatic DNA alterations in human cancer[J]. Nature Biotechnology, 2012, 30(5): 413-421. DOI:10.1038/nbt.2203 3 YOSHIHARAK, SHAHMORADGOLIM, MARTÍNEZE, et al. Inferring tumour purity and stromal and immune cell admixture from expression data[J]. Nature Communications, 2013(4): 2612. 4 ZHANGN Q, WUH J, ZHANGW W, et al. Predicting tumor purity from methylation microarray data[J]. Bioinformatics, 2015, 31(21): 3401-3405.DOI:10.1093/bioinformatics/btv370 5 ZHENGX Q, ZHANGN Q, WUH J, et al. Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies[J]. Genome Biology, 2017, 18:17.DOI:10.1186/s13059-016-1143-5 6 DOUH X, FANGY, ZHENGX Q. Universal informative CpG sites for inferring tumor purity from DNA methylation microarray data[J]. Journal of Bioinformatics and Computational Biology, 2018, 16(3): 1750030. DOI:10.1142/s0219720017500305 7 ZHENGX Q, ZHAOQ, WUH J, et al. MethylPurify: Tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes[J]. Genome Biology, 2014, 15(8): 419. DOI:10.1186/s13059-014-0419-x 8 SUX P, ZHANGL, ZHANGJ P, et al. PurityEst: Estimating purity of human tumor samples using next-generation sequencing data[J]. Bioinformatics, 2012, 28(17): 2265-2266.DOI:10.1093/bioinformatics/bts365 9 WILSONR. Multiresolution Gaussian Mixture Models: Theory and Application[R]. Coventry: University of Warwick, 2000: 1-10. 10 ZHOUY T, FANY, CHENZ Y, et al. Multimodality prediction of chaotic time series with sparse hard-cut EM learning of the Gaussian process mixture model[J]. Chinese Physics Letter, 2017, 34(5): 050502. DOI:10.1088/0256-307x/34/5/050502 11 王凯南,金立左. 基于高斯混合模型的EM算法改进与优化[J]. 工业控制计算机, 2017, 30(5): 115-116,118. WANGK N, JINL Z. Improvement and optimization of EM algorithm based on Gaussian mixture model[J]. Industrial Control Computer, 2017, 30(5): 115-118. |
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