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PubMed2004

Analysing microarray data using modular regulation analysis.

MOTIVATION: Microarray experiments measure complex changes in the abundance of many mRNAs under different conditions. Current analysis methods cannot distinguish between direct and indirect effects on expression, or calculate the relative importance of mRNAs in effecting responses. RESULTS: Application of modular regulation analysis to microarray data reveals and quantifies which mRNA changes are important for cellular responses. The mRNAs are clustered, and then we calculate how perturbations alter each cluster and how strongly those clusters affect an output response. The product of these values quantifies how an input changes a response through each cluster. Two published datasets are analysed. Two mRNA clusters transmit most of the response of yeast doubling time to galactose; one contains mainly galactose metabolic genes, and the other a regulatory gene. Analysis of the response of yeast relative fitness to 2-deoxy-D-glucose reveals that control is distributed between several mRNA clusters, but experimental error limits statistical significance.

Algorithms↗

PubMed2006

Unsupervised image-set clustering using an information theoretic framework.

In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.

Algorithms↗

PubMed2006

Fast agglomerative clustering using a k-nearest neighbor graph.

We propose a fast agglomerative clustering method using an approximate nearest neighbor graph for reducing the number of distance calculations. The time complexity of the algorithm is improved from O(tauN2) to O(tauNlogN) at the cost of a slight increase in distortion; here, tau denotes the number of nearest neighbor updates required at each iteration. According to the experiments, a relatively small neighborhood size is sufficient to maintain the quality close to that of the full search.

Algorithms↗

PubMed2001

A multiparametric and multiresolution segmentation algorithm of 3-D ultrasonic data.

An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data.

Algorithms↗

PubMed2004

EyeSite: a semi-automated database of protein families in the eye.

The EyeSite is a web-based database of protein families for proteins that function in the eye and their homologous sequences. The resource clusters proteins at different levels of homology in order to facilitate functional annotation of sequences and modelling of proteins from structural homologues. Eye proteins are organized into the tissue types in which they function and are clustered into homologous families using a novel protocol employing the TribeMCL algorithm. Homologous families are further subdivided into sequence clusters for which multiple sequence alignments are generated. Structural annotations from the CATH domain database are provided for nearly 90% of the sequences, and protein family annotations from the Pfam database for approximately 86%. Homology models have also been generated where appropriate. The EyeSite is stored in a relational database and is extensively linked to other online bioinformatics resources to help relate allelic variants, annotations and clinical details to the derived data in the database. The EyeSite is available for online search, sequence information and model retrieval at http://eyesite.cryst.bbk.ac.uk/.

Amino Acid Sequence↗

PubMed2006

Binary state pattern clustering: a digital paradigm for class and biomarker discovery in gene microarray studies of cancer.

Class and biomarker discovery continue to be among the preeminent goals in gene microarray studies of cancer. We have developed a new data mining technique, which we call Binary State Pattern Clustering (BSPC) that is specifically adapted for these purposes, with cancer and other categorical datasets. BSPC is capable of uncovering statistically significant sample subclasses and associated marker genes in a completely unsupervised manner. This is accomplished through the application of a digital paradigm, where the expression level of each potential marker gene is treated as being representative of its discrete functional state. Multiple genes that divide samples into states along the same boundaries form a kind of gene-cluster that has an associated sample-cluster. BSPC is an extremely fast deterministic algorithm that scales well to large datasets. Here we describe results of its application to three publicly available oligonucleotide microarray datasets. Using an alpha-level of 0.05, clusters reproducing many of the known sample classifications were identified along with associated biomarkers. In addition, a number of simulations were conducted using shuffled versions of each of the original datasets, noise-added datasets, as well as completely artificial datasets. The robustness of BSPC was compared to that of three other publicly available clustering methods: ISIS, CTWC and SAMBA. The simulations demonstrate BSPC's substantially greater noise tolerance and confirm the accuracy of our calculations of statistical significance.

Algorithms↗

PubMed2006

PEPPeR, a platform for experimental proteomic pattern recognition.

Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.

Algorithms↗

PubMed2004

Clustering of diverse genomic data using information fusion.

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.

Algorithms↗

PubMed2002

Data mining to support simulation modeling of patient flow in hospitals.

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.

Algorithms↗

PubMed2001

Nonparametric techniques to extract fuzzy rules for breast cancer diagnosis problem.

This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is constructed around each prototype. Finally, these clusters are interpreted as the fuzzy rules that relate the prognostic factors and the diagnosis results. The advantages of the proposed algorithm are, (a) there is no need to know the structure of the training data, (b) the number of fuzzy rules does not increase with the increase of the number of input dimensions, and (c) small number of fuzzy rules is generated. With the three generated fuzzy rules, 96.20% classification efficiency is achieved, which is comparable to other rule generation techniques.

Algorithms↗

PubMed2006

Identification of protein biomarkers in Dupuytren's contracture using surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).

BACKGROUND: To study the protein expression profiles associated with Dupuytren's contracture (DC) to identify potential disease protein biomarkers (PBM) using a proteomic technology--Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). METHODS: Normal and disease palmar fascia from DC patients were analyzed using Ciphergen's SELDI-TOF-MS Protein Biological System II (PBSII) ProteinChip reader. Analysis of the resulting SELDI-TOF spectra was carried out using the peak cluster analysis program (BioMarker Wizard, Ciphergen). Common peak clusters were then filtered using a bootstrap algorithm called SAM (Significant Analysis of Microarrays) for increased fidelity in our analysis. RESULTS: Several differentially expressed low molecular weight (<20 kDa) tissue proteins were identified. Spectra generated using both ZipTipC18 aided Au array and WCX2 array based SELDI-TOF-MS were reproducible, with an average peak cluster mass standard deviation for both methods of <1.74 x10(-4). Initial peak cluster analysis of the SELDI spectra identified both up-(14) and down-(3)regulated proteins associated with DC. Further analysis of the peak cluster data using the bootstrap algorithm SAM identified three disease-associated protein features (4600.8 Da, 10254.5 Da, and 11405.1 Da) that were elevated (5.45, 11.7, and 4.28 fold, respectively, with a 0% median false discovery rate). CONCLUSION: SELDI-TOF-MS identified three potential low molecular weight tissue protein markers (p4.6DC, p1ODC, p11.7DC) for DC. The ability of SELDI-TOF-MS to resolve low molecular weight proteins suggests that the method may provide a means of deciphering the biomarker-rich low molecular weight region of the human proteome. Application of such novel technology may help clinicians to focus on specific molecular abnormalities in diseases with no known molecular pathogenesis, and uncover therapeutic and/or diagnostic targets.

Algorithms↗

PubMed2005

Knowledge guided analysis of microarray data.

To microarray expression data analysis, it is well accepted that biological knowledge-guided clustering techniques show more advantages than pure mathematical techniques. In this paper, Gene Ontology is introduced to guide the clustering process, and thus a new algorithm capturing both expression pattern similarities and biological function similarities is developed. Our algorithm was validated on two well-known public data sets and the results were compared with some previous works. It is shown that our method has advantages in both the quality of clusters and the precision of biological annotations. Furthermore, the clustering results can be adjusted according to different stringency requirements. It is expected that our algorithm can be extended to other biological knowledge, for example, metabolic networks.

Algorithms↗

PubMed2006

Coexpressionfinder: a new algorithm for finding groups of coexpressed genes.

RESULTS: A new algorithm is developed which is intended to find groups of genes whose expression values change in a concordant manner in a series of experiments with DNA arrays. This algorithm is named as CoexpressionFinder. It can find more complete and internally coordinated groups of gene expression vectors than hierarchical clustering. Also, it finds more genes having coordinated expression. The algorithm's design allows parallel execution. AVAILABILITY: The algorithm is implemented as a Java application which is freely available at: http://www.bioinformatics.ru/cf/index.jsp and http://bioinformatics.ru/cf/index.jsp.

Algorithms↗

PubMed2006

[An algorithm based on deformable contour models for medical image segmentation].

This paper provides a new medical image segmentation algorithm using a deformable contour model, which integrates Fuzzy C-Means(FCM) Clustering technique and deformable contour model. An external fuzzy constrain is defined from the membership function value of FCM, which joins the external constrain of the deformable model and drives the deformable model towards the contour ideal edge of the object. Examples are presented to demonstrate the efficiency and feasibility of the approach on spinal MRI images and the results are encouraging.

Algorithms↗

PubMed2004

Cerebral microdialysis of patients with severe traumatic brain injury exhibits highly individualistic patterns as visualized by cluster analysis with self-organizing maps.

OBJECTIVE: To analyze patterns of cerebral microdialysis in patients with traumatic brain injury and, with a neural network methodology, investigate pattern relationships to intracranial pressure and cerebral perfusion pressure. DESIGN: Retrospective. SETTING: University hospital, adult neurosurgical intensive care unit. PATIENTS: Twenty-six patients with severe traumatic brain injury. All consecutive traumatic brain injured patients (Glasgow Coma Scale < or =8) with microdialysis monitoring, analyzing glutamate, lactate, pyruvate, and glucose in both penumbral and nonpenumbral tissue. INTERVENTIONS: None; patients received the unit's standard neurointensive care procedure. MEASUREMENTS AND MAIN RESULTS: We used 2084 hrs of complete microdialysis data sets (eight markers) to train Kohonen self-organizing maps. The self-organizing map algorithm is a data-clustering method that reduces high-dimensional information to a two-dimensional representation on a grid (map), retaining local relationships in the data. Maps were colored (overlaid) for intracranial pressure, cerebral perfusion pressure, and outcome, to explore relationships with underlying microdialysis patterns. The maps exhibited a striking clustering of patients, with unique microdialysis patterns that were recognizable throughout the analysis period. This also held true for most microdialysis patterns characteristic of ischemia. These patients with ischemic patterns can have good outcomes, suggesting a disparity between microdialysis values and severity of traumatic brain injury. CONCLUSION: Using an artificial neural network-like clustering technique, Kohonen self-organizing maps, we have shown that cerebral microdialysis, in traumatic brain injury, exhibits strikingly individualistic patterns that are identifiable throughout the analysis period. Because patients form their own clusters, microdialysis patterns, during periods of increased intracranial pressure or decreased cerebral perfusion pressure, will be found within these clusters. Consequently, no common pattern of microdialysis can be seen among patients within the range of our data. We suggest that these individualistic patterns reflect not only metabolic states of traumatic brain injury but also local gradients seen with small volume sampling. Future investigation should focus on relating these patterns, and movement within and from clusters, to metabolic states of the complex pathophysiology of traumatic brain injury.

Adolescent↗

PubMed2003

Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies.

The spliced alignment of expressed sequence data to genomic sequence has proven a key tool in the comprehensive annotation of genes in eukaryotic genomes. A novel algorithm was developed to assemble clusters of overlapping transcript alignments (ESTs and full-length cDNAs) into maximal alignment assemblies, thereby comprehensively incorporating all available transcript data and capturing subtle splicing variations. Complete and partial gene structures identified by this method were used to improve The Institute for Genomic Research Arabidopsis genome annotation (TIGR release v.4.0). The alignment assemblies permitted the automated modeling of several novel genes and >1000 alternative splicing variations as well as updates (including UTR annotations) to nearly half of the approximately 27 000 annotated protein coding genes. The algorithm of the Program to Assemble Spliced Alignments (PASA) tool is described, as well as the results of automated updates to Arabidopsis gene annotations.

Algorithms↗

PubMed1994

Cost-efficient designs of cluster unit trials.

BACKGROUND: Large field trials that randomize naturally occurring clusters such as communities, worksites, or schools are becoming widely accepted for evaluating complex interventions. The within-cluster measurement of individuals typically uses a cohort, followed throughout the trial or cross-sectional samples selected independently at each time point. The relative costs of these approaches is of concern in designing such trials. METHODS: This paper takes the unified model for analyzing large cluster unit trials developed by Feldman and McKinlay (Stat Med 1993) and combines the resulting expression for the variance of the treatment effect with a simple cost function into an algorithm that produces the optimal trial design in terms of the number of clusters and the number of observations per cluster. Using the unified model developed in the prior paper, this algorithm also allows direct comparison of the cost of designs with equivalent precision. In particular, designs that use cohorts in each cluster unit and observe cohort members over time are contrasted with designs that draw independent cross-sectional samples from each cluster at each time point. RESULTS: Using the algorithm and a realistic design problem, it is demonstrated that cohort designs are more cost efficient for short trials and high (> or = 0.75) autocorrelations. CONCLUSIONS: The power of the algorithm in designing cost-efficient cluster unit trials is well demonstrated. Estimates of variance and cost components from prior trials need to be readily accessible for use in the algorithm, for planning subsequent trials.

Algorithms↗

PubMedDate not supplied

A comparative study on the application of hierarchical-agglomerative clustering approaches to organize outputs of reiterated docking runs.

Reiterated runs of standard docking protocols usually provide a collection of possible binding modes rather than pinpoint a single solution. Usually, this ensemble is then ranked by means of an energy-based scoring function. However, since many degrees of approximation have to be introduced in the computation of the binding free energy, scoring functions cannot always rank the experimental pose among the top scorers. Cluster analysis might help to overcome this limit, provided that data clusterability has been earlier assessed. In this paper, first, we present a modified version of a test earlier developed by Hopkins to assess whether or not docking outputs show the natural tendency to be grouped in clusters. Then, we report the results of a comparative study on the application of different hierarchical-agglomerative cluster rules to partition docking outputs. The rule that was able to best manage the observed data was finally applied to the whole ensemble of poses collected from several docking tools. The combination of the average linkage rule with the cutting function developed by Sutcliffe and co-workers turned out to be an approach that meets all of the criteria required for a robust clustering protocol. Furthermore, a consensus clustering allowed us to identify the pose closest to the experimental one within a statistically significant cluster, whose number was always of few units.

Adenine↗