Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Clustering Algorithms”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources
PubMed2003

Diametrical clustering for identifying anti-correlated gene clusters.

MOTIVATION: Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. RESULTS: We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Algorithms↗
PubMedDate not supplied

An adaptive strategy for single- and multi-cluster gene assignment.

Strict assignment of genes to one class, dimensionality reduction, a priori specification of the number of classes, the need for a training set, nonunique solution, and complex learning mechanisms are some of the inadequacies of current clustering algorithms. Existing algorithms cluster genes on the basis of high positive correlations between their expression patterns. However, genes with strong negative correlations can also have similar functions and are most likely to have a role in the same pathways. To address some of these issues, we propose the adaptive centroid algorithm (ACA), which employs an analysis of variance (ANOVA)-based performance criterion. The ACA also uses Euclidian distances, the center-of-mass principle for heterogeneously distributed mass elements, and the given data set to give unique solutions. The proposed approach involves three stages. In the first stage a two-way ANOVA of the gene expression matrix is performed. The two factors in the ANOVA are gene expression and experimental condition. The residual mean squared error (MSE) from the ANOVA is used as a performance criterion in the ACA. Finally, correlated clusters are found based on the Pearson correlation coefficients. To validate the proposed approach, a two-way ANOVA is again performed on the discovered clusters. The results from this last step indicate that MSEs of the clusters are significantly lower compared to that of the fibroblast-serum gene expression matrix. The ACA is employed in this study for single- as well as multi-cluster gene assignments.

Algorithms↗
PubMed2004

Analysis of the root nodule-enhanced transcriptome in soybean.

For high throughput screening of root nodule-enhanced genes, cDNA libraries specific for three different developmental stages of soybean root nodules were constructed after inoculation with Bradyrhizobium japonicum USDA110. 5,469 cDNA clones were sequenced and grouped into 2,511 non-redundant (nr) ESTs consisting of 769 contigs and 1,742 singletons. Using similarity searches against several public databases we constructed a functional classification of the ESTs into root nodule-specific nodulin genes, stress-responsive genes and genes related to carbon and nitrogen metabolism. We also constructed a cDNA microarray with 382 selected clones that appeared to be up-regulated in the root nodule. Using the microarray we compared the transcript levels of uninfected roots and root nodules from four developmental stages. We identified 81 genes that were differentially expressed, and grouped them into seven clusters according to the similarity of their expression profiles, using a hierarchical clustering algorithm. Clusters 1, 2, 3, and 6, comprised of 58 genes, showed root nodule-enhanced expression. The information from this study will be used to analyze the roles of root nodule-specific genes and signaling pathways during root nodule development.

Databases, Nucleic Acid↗
PubMed2005

A Weighted Sum Validity Function for clustering with a Hybrid Niching Genetic Algorithm.

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.

Algorithms↗
PubMed1994

Deriving non-homogeneous DNA Markov chain models by cluster analysis algorithm minimizing multiple alignment entropy.

Non-homogeneous Markov chain models can represent biologically important regions of DNA sequences. The statistical pattern that is described by these models is usually weak and was found primarily because of strong biological indications. The general method for extracting similar patterns is presented in the current paper. The algorithm incorporates cluster analysis, multiple alignment and entropy minimization. The method was first tested using the set of DNA sequences produced by Markov chain generators. It was shown that artificial gene sequences, which initially have been randomly set up along the multiple alignment panels, are aligned according to the hidden triplet phase. Then the method was applied to real protein-coding sequences and the resulting alignment clearly indicated the triplet phase and produced the parameters of the optimal 3-periodic non-homogeneous Markov chain model. These Markov models were already employed in the GeneMark gene prediction algorithm, which is used in genome sequencing projects. The algorithm can also handle the case in which the sequences to be aligned reveal different statistical patterns, such as Escherichia coli protein-coding sequences belonging to Class II and Class III. The algorithm accepts a random mix of sequences from different classes, and is able to separate them into two groups (clusters), align each cluster separately, and define a non-homogeneous Markov chain model for each sequence cluster.

Algorithms↗
PubMed2004

Fast iterative gene clustering based on information theoretic criteria for selecting the cluster structure.

Grouping of genes into clusters according to their expression levels is important for deriving biological information, e.g., on gene functions based on microarray and other related analyses. The paper introduces the selection of the number of clusters based on the minimum description length (MDL) principle for the selection of the number of clusters in gene expression data. The main feature of the new method is the ability to evaluate in a fast way the number of clusters according to the sound MDL principle, without exhaustive evaluations over all possible partitions of the gene set. The estimation method can be used in conjunction with various clustering algorithms. A recent clustering algorithm using principal component analysis, the "gene shaving" (GS) procedure, can be modified to make use of the new MDL estimation method, replacing the Gap statistics originally used in GS algorithm. The resulting clustering algorithm is shown to perform better than GS-Gap and CEM (classification expectation maximization), in the simulations using artificial data. The proposed method is applied to B-cell differentiation data, and the resulting clusters are compared with those found by self-organizing maps (SOM).

Algorithms↗
PubMed2001

Validating clustering for gene expression data.

MOTIVATION: Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. The remaining condition is used to assess the predictive power of the resulting clusters-meaningful clusters should exhibit less variation in the remaining condition than clusters formed by chance. RESULTS: We successfully applied our methodology to compare six clustering algorithms on four gene expression data sets. We found our quantitative measures of cluster quality to be positively correlated with external standards of cluster quality.

Algorithms↗
PubMed2001

Constructive genetic algorithm for clustering problems.

Genetic algorithms (GAs) have recently been accepted as powerful approaches to solving optimization problems. It is also well-accepted that building block construction (schemata formation and conservation) has a positive influence on GA behavior. Schemata are usually indirectly evaluated through a derived structure. We introduce a new approach called the Constructive Genetic Algorithm (CGA), which allows for schemata evaluation and the provision of other new features to the GA. Problems are modeled as bi-objective optimization problems that consider the evaluation of two fitness functions. This double fitness process, called fg-fitness, evaluates schemata and structures in a common basis. Evolution is conducted considering an adaptive rejection threshold that contemplates both objectives and attributes a rank to each individual in population. The population is dynamic in size and composed of schemata and structures. Recombination preserves good schemata, and mutation is applied to structures to get population diversification. The CGA is applied to two clustering problems in graphs. Representation of schemata and structures use a binary digit alphabet and are based on assignment (greedy) heuristics that provide a clearly distinguished representation for the problems. The clustering problems studied are the classical p-median and the capacitated p-median. Good results are shown for problem instances taken from the literature.

Algorithms↗