Gibbs Sampling Based Bayesian Biclustering of Gene Expression Data

被引：0

作者：

Chen, Daoyuan ^{[1
]}

Liu, Qinyi ^{[1
]}

Meng, Jia ^{[2
]}

Su, Jionglong ^{[3
]}

机构：

[1] Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou 215123, Peoples R China

[2] Xian Jiaotong Liverpool Univ, Dept Biol Sci, Suzhou 215123, Peoples R China

[3] Xian Jiaotong Liverpool Univ, XJTLU Entrepreneur Coll Taicang, Sch AI & Adv Comp, Suzhou 215123, Peoples R China

来源：

2020 13TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2020) | 2020年

关键词：

Biclustering; Bayesian inference; Gibbs sampling; Multivariate Gaussian distribution;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.

引用

页码：790 / 795

页数：6

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