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PubMed2004

Fast k-nearest neighbor classification using cluster-based trees.

Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.

Algorithms↗
PubMed2001

Causality assessment of suspected virus transmission by human plasma products.

BACKGROUND: Causality assessment of reports on suspected virus transmission is crucial for early detection of infectious plasma products. Commonly used algorithms, such as the WHO criteria, do not meet the specific requirements for causality assessment of suspected virus transmission. STUDY DESIGN AND METHODS: A special algorithm, based on nucleic acid amplification and gene sequencing technology, effectiveness of validated virus-inactivation methods, empirical data concerning the safety record of the product, and information on batch-related infection clusters, was developed. The algorithm is focused on laboratory test results or otherwise standardized data, with few clinical data being required. To facilitate practical application, the algorithm has been converted into a graphical decision tree. RESULTS: The feasibility of the algorithm is shown by causality assessment of sample cases. Three cases are presented with the details of each case used in the 12-question checklist. The answers provided by the checklist led to the causality classification. CONCLUSION: The algorithm is a tool for evaluating reports of suspected virus transmission in a standardized manner. It thus has the potential to improve early signal detection in pharmacovigilance of plasma products by confirmation or exclusion of suspected infectivity in most cases.

Adult↗
PubMed2004

Clusters in a magnetic toy model for binary granular piles.

Results on a generalized magnetically controlled ballistic deposition model of granular piles are reported in order to search for the effect of "spin flip" probability q in building a granular pile. Two different regimes of spin cluster site distributions have been identified, a borderline q(c) (betaJ) where J is the interaction potential strength.

Algorithms↗
PubMed1994

An improved system for exon recognition and gene modeling in human DNA sequences.

A new version of the GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992; Uberbacher et al., 1993), called GRAIL II, has recently been developed (Xu et al., 1994). GRAIL II is a hybrid AI system that supports a number of DNA sequence analysis tools including protein-coding region recognition, PolyA site and transcription promoter recognition, gene model construction, translation to protein, and DNA/protein database searching capabilities. This paper presents the core of GRAIL II, the coding exon recognition and gene model construction algorithms. The exon recognition algorithm recognizes coding exons by combining coding feature analysis and edge signal (acceptor/donor/translation-start sites) detection. Unlike the original GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992), this algorithm uses variable-length windows tailored to each potential exon candidate, making its performance almost exon length-independent. In this algorithm, the recognition process is divided into four steps. Initially a large number of possible coding exon candidates are generated. Then a rule-based prescreening algorithm eliminates the majority of the improbable candidates. As the kernel of the recognition algorithm, three neural networks are trained to evaluate the remaining candidates. The outputs of the neural networks are then divided into clusters of candidates, corresponding to presumed exons. The algorithm makes its final prediction by picking the best canadidate from each cluster. The gene construction algorithm (Xu, Mural and Uberbacher, 1994) uses a dynamic programming approach to build gene models by using as input the clusters predicted by the exon recognition algorithm. Extensive testing has been done on these two algorithms.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1994

Synchrony detection in neural assemblies.

The identification of synchronously active neural assemblies in simultaneous recordings of neuron activities is an important research issue and a difficult algorithmic problem. A gravitational analysis method has been developed to detect and identify groups of neurons that tend to generate action potentials in near-synchrony from among a larger population of simultaneously recorded units. In this paper, an improved algorithm is used for the gravitational clustering method and its performance is characterized. Whereas the original algorithm ran in n3 time (n = the number of neurons), the new algorithm runs in n2 time. Neurons are represented as particles in n-space that 'gravitate' towards one another whenever near-synchronous electrical activity occurs. Ensembles of neurons that tend to fire together then become clustered together. The gravitational technique not only identifies the synchronous groups present but also shows graphically the changing activity patterns and changing synchronies.

Action Potentials↗
PubMed2005

An EM algorithm for the block mixture model.

Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the Classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.

Algorithms↗
PubMed2004

SPICKER: a clustering approach to identify near-native protein folds.

We have developed SPICKER, a simple and efficient strategy to identify near-native folds by clustering protein structures generated during computer simulations. In general, the most populated clusters tend to be closer to the native conformation than the lowest energy structures. To assess the generality of the approach, we applied SPICKER to 1489 representative benchmark proteins </=200 residues that cover the PDB at the level of 35% sequence identity; each contains up to 280,000 structure decoys generated using the recently developed TASSER (Threading ASSembly Refinement) algorithm. The best of the top five identified folds has a root-mean-square deviation from native (RMSD) in the top 1.4% of all decoys. For 78% of the proteins, the difference in RMSD from native to the identified models and RMSD from native to the absolutely best individual decoy is below 1 A; the majority belong to the targets with converged conformational distributions. Although native fold identification from divergent decoy structures remains a challenge, our overall results show significant improvement over our previous clustering algorithms.

Algorithms↗
PubMed2006

DNA microarray technology for the microbiologist: an overview.

DNA microarrays have found widespread use as a flexible tool to investigate bacterial metabolism. Their main advantage is the comprehensive data they produce on the transcriptional response of the whole genome to an environmental or genetic stimulus. This allows the microbiologist to monitor metabolism and to define stimulons and regulons. Other fields of application are the identification of microorganisms or the comparison of genomes. The importance of this technology increases with the number of sequenced genomes and the falling prices for equipment and oligonucleotides. Knowledge of DNA microarrays is of rising relevance for many areas in microbiological research. Much literature has been published on various specific aspects of this technique that can be daunting to the casual user and beginner. This article offers a comprehensive outline of microarray technology for transcription analysis in microbiology. It shortly discusses the types of DNA microarrays available, the printing of custom arrays, common labeling strategies for targets, hybridization, scanning, normalization, and clustering of expression data.

Algorithms↗
PubMed2003

Clustering huge data sets for parametric PET imaging.

A new preprocessing clustering technique for quantification of kinetic PET data is presented. A two-stage clustering process, which combines a precluster and a classic hierarchical cluster analysis, provides data which are clustered according to a distance measure between time activity curves (TACs). The resulting clustered mean TACs can be used directly for estimation of kinetic parameters at the cluster level, or to span a vector space that is used for subsequent estimation of voxel level kinetics. The introduction of preclustering significantly reduces the overall time for clustering of multiframe kinetic data. The efficiency and superiority of the preclustering scheme combined with thresholding is validated by comparison of the results for clustering both with and without preclustering for FDG-PET brain data of 13 healthy subjects.

Algorithms↗
PubMed2006

Ground states and defect energies of the two-dimensional XY spin glass from a quasiexact algorithm.

We employ a novel algorithm using a quasiexact embedded-cluster matching technique as minimization method within a genetic algorithm to reliably obtain numerically exact ground states of the Edwards-Anderson XY spin-glass model with bimodal coupling distribution for square lattices of up to 28 x 28 spins. Contrary to previous conjectures, the ground state of each disorder replica is nondegenerate up to a global O(2) rotation. The scaling of spin and chiral defect energies induced by applying several different sets of boundary conditions exhibits strong crossover effects. This suggests that previous calculations have yielded results far from the asymptotic regime. The novel algorithm and the aspect-ratio scaling technique consistently give theta(s)=-0.308(30) and theta(c)=-0.114(16) for the spin and chiral stiffness exponents, respectively.

Journal Article↗
PubMed2000

Application of a Fourier series to analysis of the relationship between mandibular form and facial morphology.

The shape of the mandible is used by orthodontists as an aid to orthodontic treatment planning, with different shapes displaying different growth characteristics and being associated with different functional patterns. However, the quantification of shape using conventional linear and angular variables does not always include details of some of the more subtle aspects of form. Fourier analysis enables accurate representation, with few coefficients, of a mandibular outer line from articulare to infradentale. The aim of this study was to investigate, using a cluster analysis, the relationship between variation in mandibular form and overall facial morphology. A cluster analysis of the Fourier amplitudes classified mandibular forms into one of seven groups for both males and females. The cluster analysis showed that the gonial angle and the inclination of lower incisor were associated with facial type according to the mandibular variables. Furthermore, the Fourier descriptions of mandibular form could be related to the observed variation in facial form, each of the clusters based on mandibular form being associated with demonstrable differences in dentofacial morphology.

Adult↗
PubMed2003

A computerized image analysis system for quantitative analysis of cells in histological brain sections.

We propose a reliable method for automatic counting of cells in brain sections labeled with different antibodies (against NeuN, parvalbumin, GABA and c-Fos) and in Nissl-staining. Images of stained sections are converted to binary images by thresholding. Clusters of 'ON pixels' (value of 1) corresponding to cell bodies are selected based on size. The parameters of the algorithm (intensity range and cluster-size) are adjusted for different methods of staining according to expert knowledge. The automatic cell counting method (ACCM) provides correct counting results, as demonstrated by a comparison of computational results with counts gained by human experimenters and with a commercially available image analysis system. On the basis of ACCM counts, small and perhaps physiologically relevant differences in the number of labeled cells can be revealed, as demonstrated here for the GABAergic system following electrical stimulation.

Algorithms↗
PubMed2006

Ab initio determination of solid-state nanostructure.

Advances in materials science and molecular biology followed rapidly from the ability to characterize atomic structure using single crystals. Structure determination is more difficult if single crystals are not available. Many complex inorganic materials that are of interest in nanotechnology have no periodic long-range order and so their structures cannot be solved using crystallographic methods. Here we demonstrate that ab initio structure solution of these nanostructured materials is feasible using diffraction data in combination with distance geometry methods. Precise, sub-ångström resolution distance data are experimentally available from the atomic pair distribution function (PDF). Current PDF analysis consists of structure refinement from reasonable initial structure guesses and it is not clear, a priori, that sufficient information exists in the PDF to obtain a unique structural solution. Here we present and validate two algorithms for structure reconstruction from precise unassigned interatomic distances for a range of clusters. We then apply the algorithms to find a unique, ab initio, structural solution for C60 from PDF data alone. This opens the door to sub-ångström resolution structure solution of nanomaterials, even when crystallographic methods fail.

Journal Article↗
PubMed2005

Performance of a mixed effects logistic regression model for binary outcomes with unequal cluster size.

When a clustered randomized controlled trial is considered at a design stage of a clinical trial, it is useful to consider the consequences of unequal cluster size (i.e., sample size per cluster). Furthermore, the assumption of independence of observations within cluster does not hold, of course, because the subjects share the same cluster. Moreover, when the clustered outcomes are binary, a mixed effect logistic regression model is applicable. This article compares the performance of a maximum likelihood estimation of the mixed effects logistic regression model with equal and unequal cluster sizes. This was evaluated in terms of type I error rate, power, bias, and standard error through computer simulations that varied treatment effect, number of clusters, and intracluster correlation coefficients. The results show that the performance of the mixed effects logistic regression model is very similar, regardless of inequality in cluster size. This is illustrated using data from the Prevention Of Suicide in Primary care Elderly: Collaborative Trial (PROSPECT) study.

Algorithms↗
PubMed1997

An automated procedure for cluster analysis of multivariate satellite data.

This paper studies the applicability of available cluster analysis methods on multivariate satellite data. Four different methods are studied, these are the Principal Component Analysis (PCA), K-means, Self Organizing Maps (SOM) and the Adaptive Resonance Theory (ART). Special focus is placed on the usefulness of these cluster analysis methods, that is, if the results generated by these methods are relevant and are of help to the physical analysis of the data. A combined SOM adaptive dynamic K-means procedure suitable for automated cluster analysis is presented. This new method is capable of achieving useful partition of multivariate satellite data which has not previously been studied.

Algorithms↗
PubMed2004

Kernel-based self-organized maps trained with supervised bias for gene expression data analysis.

Self-Organized Maps (SOMs) are a popular approach for analyzing genome-wide expression data. However, most SOM based approaches ignore prior knowledge about functional gene categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known functional characteristics of genes. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.

Algorithms↗
PubMed2025

De novo clustering of large long-read transcriptome datasets with isONclust3.

MOTIVATION: Long-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g. cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies. RESULTS: We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, isONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256&#x2009;Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/aljpetri/isONclust3.

Algorithms↗