Phantom: investigating heterogeneous gene sets in time-course data
نویسندگان
چکیده
منابع مشابه
Clustering of Time-Course Gene Expression Data
Microarray experiments have been used to measure genes’ expression levels under different cellular conditions or along certain time course. Initial attempts to interpret these data begin with grouping genes according to similarity in their expression profiles. The widely adopted clustering techniques for gene expression data include hierarchical clustering, self-organizing maps, and K-means clu...
متن کاملTime-Course Gene Set Analysis for Longitudinal Gene Expression Data
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estima...
متن کاملApproaches to clustering gene expression time course data
Conventional techniques to cluster gene expression time course data have either ignored the time aspect, by treating time points as independent, or have used parametric models where the model complexity has to be fixed beforehand. In this thesis, we have applied a non-parametric version of the traditional hidden Markov model (HMM), called the hierarchical Dirichlet process hidden Markov model (...
متن کاملModel-Driven Clustering of Time-Course Gene Expression Data
Anne Badel-Chagnon , Gaëlle Lelandais , Serge Hazout and Pierre Vincens Equipe de Bioinformatique Génomique et Moléculaire, Inserm E0346, Université Paris 7, case 7113, 2 Place Jussieu, 75251 Paris, France Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue d’Ulm, 75230 Paris Cedex 05, France Département de Biologie (FR36), Ecole Normale Supérieure, 46 rue d’Ul...
متن کاملCluster-based network model for time-course gene expression data.
We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2017
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btx348