Bayesian model-based tight clustering for time course data
نویسندگان
چکیده
منابع مشابه
Bayesian model-based tight clustering for time course data.
Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, whi...
متن کاملModel-based clustering of time-course RNA-seq data
The next generation sequencing technology (RNA-seq) provides absolute quantification of gene expression using counts of read. Transcriptome studies are switching to rely on RNA-seq rather than microarrays since RNA-seq has higher sensitivity and dynamic range, with lower technical variation and thus higher precision than microarrays. Limited work has been done on expression analysis of longitud...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملClustering time-course Microarray data using functional Bayesian infinite mixture model
This paper presents a new Bayesian, infinite mixture model based, clustering approach specifically designed for time-course microarray data. The problem is to group together genes which have “similar” expression profiles given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a nonparametric regression ...
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 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: Computational Statistics
سال: 2009
ISSN: 0943-4062,1613-9658
DOI: 10.1007/s00180-009-0159-7