Sparse factor model for co-expression networks with an application using prior biological knowledge
作者: Yuna BlumMagalie Houée-BigotDavid Causeur
作者单位: 11 Department of Medicine , David Geffen School of Medicine, A2-237 Center for Health Sciences, University of California , 10833 Le Conte Avenue, Los Angeles, CA 90095-1679, USA
22Agrocampus Ouest, IRMAR, UMR 6625 CNRS, 65 rue de St-Brieuc CS84215, 35042 Rennes Cedex, France
刊名: Statistical Applications in Genetics and Molecular Biology, 2016, Vol.15 (3), pp.253-272
来源数据库: De Gruyter Journal
DOI: 10.1515/sagmb-2015-0002
关键词: factor modelgene ontologyhigh dimensionregularized estimationrelevance networksparsity
原始语种摘要: Abstract Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such networks can be very insightful for the deep understanding of interactions between genes. Because genes-gene interactions is often viewed as joint contributions to known biological mechanisms, inference on the dependence among gene expressions is expected to be consistent to some extent with the functional characterization of genes which can be derived from ontologies (GO, KEGG, …). The present paper introduces a sparse factor model as a general framework either to account for a prior knowledge on joint contributions of modules of genes to latent biological processes or to infer on the corresponding co-expression network. We...
全文获取路径: De Gruyter 

  • expression 表示
  • biological 生物学的
  • factor 因素
  • knowledge 知识
  • functional 功能的
  • alternative 可选择的
  • understanding 理解
  • either 任何
  • sparse 稀疏的
  • clustering 聚类