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PubMed2002

On computing the fuzzifier in downward arrow FLVQ: a data driven approach.

Clustering is an important research area that has practical applications in many fields. Fuzzy clustering has shown advantages over crisp and probabilistic clustering, especially when there are significant overlaps between clusters. Most analytic fuzzy clustering approaches are derived from Bezdek's fuzzy c-means algorithm. One major factor that influences the determination of appropriate clusters in these approaches is an exponent parameter, called the fuzzifier. To our knowledge, no theoretical reason leading to an optimal setting of this parameter is available. This paper presents the development of an heuristic scheme for determining the fuzzifier. This scheme creates close interactions between the fuzzifier and the data set to be clustered. Experimental results in clustering IRIS data and in code book design required for image compression reveal a good performance of our proposal.

Algorithms↗

PubMed2000

A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters.

The availability of computerized knowledge on biochemical pathways in the KEGG database opens new opportunities for developing computational methods to characterize and understand higher level functions of complete genomes. Our approach is based on the concept of graphs; for example, the genome is a graph with genes as nodes and the pathway is another graph with gene products as nodes. We have developed a simple method for graph comparison to identify local similarities, termed correlated clusters, between two graphs, which allows gaps and mismatches of nodes and edges and is especially suitable for detecting biological features. The method was applied to a comparison of the complete genomes of 10 microorganisms and the KEGG metabolic pathways, which revealed, not surprisingly, a tendency for formation of correlated clusters called FRECs (functionally related enzyme clusters). However, this tendency varied considerably depending on the organism. The relative number of enzymes in FRECs was close to 50% for Bacillus subtilis and Escherichia coli, but was <10% for SYNECHOCYSTIS: and Saccharomyces cerevisiae. The FRECs collection is reorganized into a collection of ortholog group tables in KEGG, which represents conserved pathway motifs with the information about gene clusters in all the completely sequenced genomes.

Algorithms↗

PubMed2000

Cluster inference methods and graphical models evaluated on NCI60 microarray gene expression data.

At present, there is a lack of a sound methodology to infer causal gene expression relationships on a genome wide basis. We address this first by examining the behaviour of some of the latest and fastest algorithms for tree and cluster analysis, particularly hierarchical methods popular in phylogenetics. Combined with these are two novel distances based on partial, rather than full, correlations. Theoretically, partial correlations should provide better evidence for regulatory genetic links than standard correlations. To compare the clusters obtained by many alternative methods we use tree consensus methods. To compare methods of analysis we used tree partition metrics followed by another level of clustering. These, and a tree fit metric, all suggest that the new distances give quite different trees than those usually obtained. In the second part we consider graphical modeling of the interactions of important genes of the cell cycle. Despite the models seeming to fit well on occasions, and despite the experimental error structure seeming close to multivariate normal, there are considerable problems to overcome. Latent variables, in this case important genes missing from the analysis, are inferred to have a strong effect on the partial correlations. Also, the data show clear evidence of sampling distributions conditional on the status of important cancer related genes, including TP53. Without full information on which genes are wild type the appropriate models cannot be fitted. These findings point to the need to include and distinguish not only all relevant genes but also all splice variants in the design phase of a microarray analysis. Failure to do so will induce problems similar to both latent variables and conditional distributions.

Algorithms↗

PubMed1994

Quantitative electrophysiological characteristics and subtyping of schizophrenia.

Quantitative descriptors of resting electroencephalogram (EEG) (QEEG) and event-related potentials (QERP) to visual and auditory stimuli were obtained from normal subjects and 94 chronic schizophrenic patients on medication, 25 chronic schizophrenics off medication, and 15 schizophrenics with no history of medication. These schizophrenic groups showed a high incidence of neurometric features that were significantly deviant from normative values. Multivariate discriminant analysis using these features successfully separated the schizophrenic patients from normals with high accuracy in independent replication. The data from the medicated group were subjected to cluster analysis. Newly developed algorithms were used for objective selection of the most effective set of variables for clustering and the optimum number of clusters to be sought. Five clusters were obtained, containing roughly equivalent proportions of the sample with markedly different QEEG profiles. The whole sample was then classified into these clusters. Each cluster contained patients both on and off medication, but patients who had never been medicated were classified into only three of these clusters. No significant clinical or demographic differences were found between members of the five clusters; however, clear differences in QERP profiles were seen. These results are described in detail and possible physiological and pharmacological implications are discussed.

Adolescent↗

PubMed1983

Numerical classification of Streptomyces and related genera.

Four hundred and seventy-five strains, which included 394 type cultures of Streptomyces and representatives of 14 other actinomycete genera, were studied. Overall similarities of these strains for 139 unit characters were determined by the SSM and SJ coefficients and clustering by the UPGMA algorithm. Test error and overlap between the phena defined were within acceptable limits. Cluster-groups were defined by the SSM coefficient at the 70.1% similarity (S) level and by the SJ coefficient at the 50% S-level. Clusters were distinguished at the 77.5% SSM and 63% SJ S-levels. Groupings obtained with the two coefficients were generally similar, but there were some changes in the definition and membership of cluster-groups and clusters. The phenetic data obtained, together with those from previous diverse studies, indicated that the genera Actinopycnidium, Actinosporangium, Chainia, Elytrosporangium, Kitasatoa and Microellobosporia should be reduced to synonyms of Streptomyces, while Intrasporangium, Nocardioides and Streptoverticillium remained as distinct genera in the family Streptomycetaceae. Nocardiopsis dassonvillei also showed strong phenetic affinity to Streptomyces, despite its chemotaxonomic differences. Actinomadura sensu stricto was phenetically distinguishable from Streptomyces and 'Nocardia' mediterranea was recognized as a taxon distinct from both these genera and from Nocardia sensu stricto. Most of the Streptomyces type cultures fell into one large cluster-group. At the 77.5% SSM S-level, they were recovered in 19 major and 40 minor clusters, with 18 strains recovered as single member clusters. The status of the latter as species was therefore confirmed. Most of the minor clusters, consisting of two to five strains, can also be regarded as species. The major clusters varied in size (from 6 to 71 strains) and in there homogeneity. Therefore, it is suggested that they be regarded as species-groups until further information is available. The results provide a basis for the reduction of the large number of Streptomyces species which have been described. They also demonstrate that the previous use of a limited number of subjectively chosen characters to define species-groups or species has resulted in artificial classifications.

Culture Media↗

PubMed2002

Automatic detection of clustered microcalcifications in digital mammograms.

In this paper we propose a new algorithm for the detection of clustered microcalcifications using mathematical morphology and artificial neural networks. Considering each mammogram as a topographic representation, each microcalcification appears as elevation constituting a regional maxima. Morphological filters are applied, in order to remove noise and regional maxima that doesn't correspond to calcifications. Each suspicious object is marked using a binary image and finally a feed forward neural network classifies every object achieving a rate of 90% true positive detections with 0.11 false positives per image.

Automation↗

PubMed2005

GeneMCL in microarray analysis.

Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5,730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms.

Algorithms↗

PubMed2006

[A study on clustering analysis of arrhythmias].

According to the characteristics of ECG analysis, large data quantum, high accuracy demand and real-time, a classified algorithm of arrhythmia based on clustering analysis is presented in this paper. According to "things-of-one-kind-come-together" principle, this algorithm uses the similarities of cases with same kind of heart disease at the same time, includes the factors of the individual difference to analyze arrhythmias by clustering QRS complex waveform and rhythm analysis as the subordinate method. Verified by eight records of MIT-BIR standard heart electricity database, the probability of correct clustering reaches above 90%, which shows that this algorithm can analyze arrhythmias effectively.

Algorithms↗

PubMed2000

Automatic detection of conserved gene clusters in multiple genomes by graph comparison and P-quasi grouping.

We previously reported two graph algorithms for analysis of genomic information: a graph comparison algorithm to detect locally similar regions called correlated clusters and an algorithm to find a graph feature called P-quasi complete linkage. Based on these algorithms we have developed an automatic procedure to detect conserved gene clusters and align orthologous gene orders in multiple genomes. In the first step, the graph comparison is applied to pairwise genome comparisons, where the genome is considered as a one-dimensionally connected graph with genes as its nodes, and correlated clusters of genes that share sequence similarities are identified. In the next step, the P-quasi complete linkage analysis is applied to grouping of related clusters and conserved gene clusters in multiple genomes are identified. In the last step, orthologous relations of genes are established among each conserved cluster. We analyzed 17 completely sequenced microbial genomes and obtained 2313 clusters when the completeness parameter P: was 40%. About one quarter contained at least two genes that appeared in the metabolic and regulatory pathways in the KEGG database. This collection of conserved gene clusters is used to refine and augment ortholog group tables in KEGG and also to define ortholog identifiers as an extension of EC numbers.

Algorithms↗

PubMed2002

NBLAST: a cluster variant of BLAST for NxN comparisons.

BACKGROUND: The BLAST algorithm compares biological sequences to one another in order to determine shared motifs and common ancestry. However, the comparison of all non-redundant (NR) sequences against all other NR sequences is a computationally intensive task. We developed NBLAST as a cluster computer implementation of the BLAST family of sequence comparison programs for the purpose of generating pre-computed BLAST alignments and neighbour lists of NR sequences. RESULTS: NBLAST performs the heuristic BLAST algorithm and generates an exhaustive database of alignments, but it only computes alignments (i.e. the upper triangle) of a possible N2 alignments, where N is the set of all sequences to be compared. A task-partitioning algorithm allows for cluster computing across all cluster nodes and the NBLAST master process produces a BLAST sequence alignment database and a list of sequence neighbours for each sequence record. The resulting sequence alignment and neighbour databases are used to serve the SeqHound query system through a C/C++ and PERL Application Programming Interface (API). CONCLUSIONS: NBLAST offers a local alternative to the NCBI's remote Entrez system for pre-computed BLAST alignments and neighbour queries. On our 216-processor 450 MHz PIII cluster, NBLAST requires ~24 hrs to compute neighbours for 850000 proteins currently in the non-redundant protein database.

Algorithms↗

PubMed2006

GeneLibrarian: an effective gene-information summarization and visualization system.

BACKGROUND: Abundant information about gene products is stored in online searchable databases such as annotation or literature. To efficiently obtain and digest such information, there is a pressing need for automated information-summarization and functional-similarity clustering of genes. RESULTS: We have developed a novel method for semantic measurement of annotation and integrated it with a biomedical literature summarization system to establish a platform, GeneLibrarian, to provide users well-organized information about any specific group of genes (e.g. one cluster of genes from a microarray chip) they might be interested in. The GeneLibrarian generates a summarized viewgraph of candidate genes for a user based on his/her preference and delivers the desired background information effectively to the user. The summarization technique involves optimizing the text mining algorithm and Gene Ontology-based clustering method to enable the discovery of gene relations. CONCLUSION: GeneLibrarian is a Java-based web application that automates the process of retrieving critical information from the literature and expanding the number of potential genes for further analysis. This study concentrates on providing well organized information to users and we believe that will be useful in their researches. GeneLibrarian is available on http://gen.csie.ncku.edu.tw/GeneLibrarian/.

Algorithms↗

PubMed2006

Bayesian search of functionally divergent protein subgroups and their function specific residues.

MOTIVATION: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. RESULTS: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. AVAILABILITY: Software 'kPax' is available at http://www.rni.helsinki.fi/jic/softa.html

Algorithms↗

PubMedDate not supplied

[Cluster analysis in biogeographical classifications].

Some unavoidable methodic and methodological problems arising at each stage of the classification of objects by the methods of hierarchical clustering are shown on the concrete (biogeographical) and abstract--numerical examples. Reasons which cause these problems and possible ways of minimization of cluster analysis artifacts are discussed. Unavoidable constrains in interpretations of dendrograms obtained by means of agglomerate algorithms are indicated.

Algorithms↗

PubMed2002

Impact of data encoding and thyroids dysfunctions.

In medical environments it is usual to previously encode some variables following medical criteria. In the context of thyroids dysfunctions, the levels of the hormones T3, T4 or TSH are usually treated as low, normal or high instead of using their numerical form, and this is a very common practice in other items. In medical environments it is also frequent to need a clustering process to analyze data. Clus-tering algorithms use always some distance or similarity coefficient to decide which elements have to be grouped in a cluster. The nature of the data determines which distance/similarity can be used, and can change results. In this paper, the impact of preprocessing numerical levels of hormones into categorical labels for clustering is studied, by means of a real sample of patients from a Hospital in Zagreb (Croatia).

Algorithms↗

PubMed1997

Domain identification by clustering sequence alignments.

A sequence databases are growing rapidly, results from sequence comparison searches using fast search methods such as BLAST and FASTA tend to be long and difficult to digest. In this paper, we present a new method to extract domain information from sequence comparison searches by clustering the resulting alignments according to their similarity to the query sequence. Efficient tree structures and algorithms are used to organize the alignment data such that structurally conserved elements can be easily identified. The hierarchical nature of the data structures used and the flexible X-Window based interface provide an efficient and intuitive means to explore the alignment data at different levels so the main domains as well as distantly related features can be explored.

Algorithms↗

PubMedDate not supplied

Sorting variables for each case: a new algorithm to calculate injury severity score (ISS) using SPSS-PC.

One of the more often used measures of multiple injuries is the injury severity score (ISS). Determination of the ISS is based on the abbreviated injury scale (AIS). This paper suggests a new algorithm to sort the AISs for each case and calculate ISS. The program uses unsorted abbreviated injury scale (AIS) levels for each case and rearranges them in descending order. The first three sorted AISs representing the three most severe injuries of a person are then used to calculate injury severity score (ISS). This algorithm should be useful for analyses of clusters of injuries especially when more patients have multiple injuries.

Abbreviated Injury Scale↗

PubMed1988

A numerical classification of the genus Bacillus.

Three hundred and sixty-eight strains of aerobic, endospore-forming bacteria which included type and reference cultures of Bacillus and environmental isolates were studied. Overall similarities of these strains for 118 unit characters were determined by the SSM, SJ and DP coefficients and clustering achieved using the UPGMA algorithm. Test error was within acceptable limits. Six cluster-groups were defined at 70% SSM, which corresponded to 69% SP and 48-57% SJ. Groupings obtained with the three coefficients were generally similar but there were some changes in the definition and membership of cluster-groups and clusters, particularly with the SJ coefficient. The Bacillus strains were distributed among 31 major (4 or more strains), 18 minor (2 or 3 strains) and 30 single-member clusters at the 83% SSM level. Most of these clusters can be regarded as taxospecies. The heterogeneity of several species, including Bacillus brevis, B. circulans, B. coagulans, B. megateriun, B. sphaericus and B. stearothermophilus, has been indicated and the species status of several taxa of hitherto uncertain validity confirmed. Thus on the basis of the numerical phenetic and appropriate (published) molecular genetic data, it is proposed that the following names be recognized; Bacillus flexus (Batchelor) nom. rev., Bacillus fusiformis (Smith et al.) comb. nov., Bacillus kaustophilus (Prickett) nom. rev., Bacillus psychrosaccharolyticus (Larkin & Stokes) nom. rev. and Bacillus simplex (Gottheil) nom. rev. Other phenetically well-defined taxospecies included 'B. aneurinolyticus', 'B. apiarius', 'B. cascainensis', 'B. thiaminolyticus' and three clusters of environmental isolates related to B. firmus and previously described as 'B. firmus-B. lentus intermediates'. Future developments in the light of the numerical phenetic data are discussed.

Bacillus↗

PubMed1989

A reappraisal of the terverticillate penicillia using biochemical, physiological and morphological features. I. Numerical taxonomy.

Three-hundred-and-forty-eight strains representing the major species of terverticillate penicillia, and including representatives of other closely and distantly related species, were included in a numerical taxonomic study. One-hundred characters were derived from morphological features, physiological and biochemical activities and SEM micrographs. Strains were compared by both Gower's coefficient and Pattern difference, and clustered using the average linkage algorithm. Thirty-seven species or species-complex clusters were recovered at approximately 70% similarity; they generally corresponded to existing taxonomic concepts. Several species were shown to contain variants or chemotypes which were often supported by differences in conidial shape and ornamentation. The use of different types of characters enabled a number of new and previously accepted species to be shown to be either variants or deteriorated examples of other species. Variation in properties both between and within species was considered, particularly in relation to strain stability.

Cluster Analysis↗