Unsupervised Cluster Analysis in Bioinformatics
نویسنده
چکیده
In this paper, we investigate the application of an unsupervised extension of the recently proposed k–windows clustering algorithm on gene expression microarray data. The k–windows algorithm is used both to identify sets of genes according to their expression in a set of samples, and to cluster samples into homogeneous groups. Experimental results and comparisons indicate that this is a promising approach.
منابع مشابه
CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure
MOTIVATION Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of...
متن کاملConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking
UNLABELLED Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with...
متن کاملAn unsupervised conditional random fields approach for clustering gene expression time series
MOTIVATION There is a growing interest in extracting statistical patterns from gene expression time-series data, in which a key challenge is the development of stable and accurate probabilistic models. Currently popular models, however, would be computationally prohibitive unless some independence assumptions are made to describe large-scale data. We propose an unsupervised conditional random f...
متن کاملClustering constrained by dependencies
Clustering is the unsupervised method of grouping data samples to form a partition of a given dataset. Such grouping is typically done based on homogeneity assumptions of clusters over an attribute space and hence the precise definition of the similarity metric affects the clusters inferred. In recent years, new formulations of clustering have emerged that posit indirect constraints on clusteri...
متن کاملUnsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data
Clustering has become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering -- the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -- is well established. This review discusses the biological motivations for and applications of these techniques to integrating gene expression data with other bi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004