NONPARAMETRIC BAYESIAN SPARSE FACTOR MODELS WITH APPLICATION TO GENE EXPRESSION MODELING
NONPARAMETRIC BAYESIAN SPARSE FACTOR MODELS WITH APPLICATION TO GENE EXPRESSION MODELING
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DOI：
10.1214/10-aoas435
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发表时间：
2011-06-01
影响因子：
1.8
通讯作者：
Ghahramani, Zoubin
中科院分区：
文献类型：
--
作者：
Knowles, David;Ghahramani, Zoubin
提出了因子分析（FA）的非参数贝叶斯扩展，其中观测数据Y被建模为潜在无限数量的隐藏因素x的线性叠加G。印度自助餐过程（IBP）被用作G的先验，以合并稀疏性并允许推断潜在特征的数量。该模型对基因表达数据建模的实用性进行了研究，使用随机生成的数据集，这些数据集基于已知的大肠杆菌稀疏连接矩阵和三个日益复杂的生物数据集。
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.