We clustered the 441 datasets, each representing a single time course, by applying the k-means algorithm and using a refined ... Identification of differentially expressed genes and cluster analysis. First, the dataset was reduced to 454 clones that showed ... Both of these clusters represent the slightest gene expression changes of our study. Within cluster 6, genes decrease the most ... 3B, Table 1). Most genes fall into clusters that display only slight expression changes (clusters 2, 4, 5, 6, 7, 10). Clusters ...http://www.jneurosci.org/content/24/26/5982
HCS clustering algorithm - Wikipedia
... is an algorithm based on graph connectivity for Cluster analysis, by first representing the similarity data in a similarity ... "Survey of clustering algorithms." Neural Networks, IEEE Transactions The CLICK clustering algorithm is an adaptation of HCS ... clustering algorithm (also known as the HCS algorithm , and other names such as Highly Connected Clusters/Components/Kernels) ... and recursively run HCS clustering algorithm on H and H'. The following animation shows how the HCS clustering algorithm ...https://en.wikipedia.org/wiki/HCS_clustering_algorithm
Systematic determination of patterns of gene expression during Drosophila embryogenesis | Genome Biology | Full Text
Systematic analysis of rigorously annotated patterns of gene expression will complement and extend the types of analyses ... that can be used to identify genes involved in developmental processes that have been missed by traditional genetic analysis. ... We present an approach for organizing the data by hierarchical clustering of annotation terms that allows us to group tissues ... Figure 8 represents one possible outcome of the clustering analysis of the annotation dataset. Filtering of the genes and ...https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-12-research0088
Clustering analysis of top 50 ranked genes is shown. Each gray block represents a tumor with higher expression than the maximum ... Using this algorithm we found that Abcb1b was among the top outliers and formed a cluster with several other genes (Fig. 3 and ... Unsupervised hierarchical cluster analysis did not separate good from poor responders (Supplementary Fig. S2). For the ... Unsupervised hierarchical cluster analysis did not separate sensitive from resistant tumors. Instead, tumors derived from the ...http://cancerres.aacrjournals.org/content/72/9/2350
Determining requirements for patient-centred care: a participatory concept mapping study | BMC Health Services Research | Full...
Analysis included the development of a similarity matrix, multidimensional scaling, hierarchical cluster analysis, selection of ... The clusters were grouped into three overarching domains, representing a cross-sectoral approach: humanity and partnership; ... ROPCCS incorporates 123 statements sorted into 13 clusters. Cluster labels were: shared responsibility for personalised health ... and achievement of each patient-centred care requirement and cluster. The study has empirically determined requirements for ...http://bmchealthservres.biomedcentral.com.preview-live.oscarjournals.springer.com/articles/10.1186/s12913-017-2741-y
Optimization of sequence alignment for simple sequence repeat regions | BMC Research Notes | Full Text
The cluster analysis resulted in completely different phylogenic trees before and after applying the new algorithm (Figure 6). ... These sequences were obtained from 26 plants representing 26 alleles. The sequences were aligned using the clustalW algorithm ... Algorithm. In this paper, we compare our new algorithm for SSR alignment with the common alignment algorithms used in other ... However, the phylogenic trees indicated that 50% of the samples showed a similar cluster before and after the new algorithm ...https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-4-239
Performance evaluation of the boundary-layer height from lidar and the weather research and forecasting model at an urban...
... we use a cluster analysis algorithm of three-day back-trajectories to determine general synoptic flow patterns over Barcelona, ... Spain arriving at heights of 0.5, 1.5, and 3 km; to represent the lower PBL, upper PBL, and lower free troposphere, ... Seven clusters are determined at each arriving altitude. Regional recirculations account for 54 % of the annual total at 0.5 km ... Lidar and radiosonde estimated PBL heights are classified under objectively-determined synoptic clusters. With the final ...https://upcommons.upc.edu/handle/2117/78947
Frontiers | Super-resolution imaging of C-type lectin spatial rearrangement within the dendritic cell plasma membrane at fungal...
Contact site CTL clusters appear as disk-shaped domains of approximately 150-175 nm in diameter. The increase in length scale ... Quantitative analysis of confocal and super-resolution images demonstrated that CTL nanodomains become concentrated in the ... Contact site CTL clusters appear as disk-shaped domains of approximately 150-175 nm in diameter. The increase in length scale ... Quantitative analysis of confocal and super-resolution images demonstrated that CTL nanodomains become concentrated in the ...https://www.frontiersin.org/articles/10.3389/fphy.2014.00046/full
A study on real-time low-quality content detection on Twitter from the users' perspective
To perform the cluster analysis, we represent the tweets with a set of features (described in details in the later section) and ... We perform EM algorithm on harvested low-quality content tweets to divide them into 4 categories. Based on which, we create a ... Cluster analysis of low-quality content. We believe it is necessary to understand users' attitudes and definitions of low- ... We designed a survey according to the cluster analysis and put it online where participants had to answer two questions related ...http://pubmedcentralcanada.ca/pmcc/articles/PMC5549928/
Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps : Journal of Data and Information Science
... generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts ... Both cluster solutions were generated in VOSviewer, using its algorithm⑫. Appendix map A-3 shows the 18-cluster basemap. ... By choosing the 18-cluster option (Figure A-3) or the five-cluster option (Figure 1), one can show the analysis at a narrow or ... Prices in € represent the retail prices valid in Germany (unless otherwise indicated). Prices are subject to change without ...https://www.degruyter.com/view/j/jdis.2017.2.issue-3/jdis-2017-0015/jdis-2017-0015.xml
Purdue e-Pubs - The Summer Undergraduate Research Fellowship (SURF) Symposium: Web-Based Interactive Social Media Visual...
Second, we extend the Social Media Analytics and Reporting Toolkit (SMART) to supporting the spatial clustering analysis and ... findings prove that today's high-resolution geo-located social media platforms can enable new types of human behavior analysis ... Identified movement patterns are extracted using clustering algorithms and represented in a visual and interactive manner. We ... Identified movement patterns are extracted using clustering algorithms and represented in a visual and interactive manner. We ...https://docs.lib.purdue.edu/surf/2017/presentations/87/
The Realities of Races
You could do a statistical cluster analysis, but that is sensitive to the population samples chosen, the individual people ... representing them, the demographic history of the populations, the assumptions of the particular algorithm, and the patterns of ... based on the assumption that a small sample of Nigerians represent "pure Africans," a small sample of Chinese represent "pure ... The extremes represent the most geographically divergent, not the purest.. In other words, this business has far more to do ...http://raceandgenomics.ssrc.org/Marks/
Clustering bioinformatics tools | Gene expression array analysis - OMICtools
Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to ... When applied to published data sets representing different cancers or disease outcomes, this algorithm constructs XMOTIFS that ... This algorithm generates clusters of sufficiently large sample sizes as required by the user and can clusters applications with ... A clustering analysis platform to promote streamlined evaluation, comparison and reproducibility of clustering results in the ...https://omictools.com/clustering-category
Detection of chronic kidney disease and disease progression - Technion Research and Development Foundation Ltd.
... linear discriminant analysis (LDA), cluster analysis, and nearest neighbor. Each possibility represents a separate embodiment ... genetic algorithms, and fuzzy logic algorithms. Each possibility represents a separate embodiment of the present invention. In ... including linear discriminant analysis (LDA), and cluster analysis including nearest neighbor. Each possibility represents a ... discriminant function analysis (DFA), linear discriminant analysis (LDA), cluster analysis, and nearest neighbor. 8. The method ...http://www.freepatentsonline.com/9689826.html
Clustering Similar Points - Stellar Parameter Analysis Management - Colby College Wiki
To get better clusters, we first ran principal components analysis to compress the data to axes that represent almost all of ... K-Means Algorithm. *instantiate k means to start with: our algorithm divides the data into k equal parts, and selects a random ... Clusters are categorical, so it is intuitive to visualize them with color. We made 'cluster' an option for the color 'axis,' ... One goal of machine learning is to cluster data points by identifying similar points. A common method of clustering is k-means ...https://wiki.colby.edu/display/SPAM/Clustering+Similar+Points
Method of increasing murine ES cell differentiation with PCL2 - Patent # 8252761 - PatentGenius
Clustering was performed using the K-means clustering algorithm. GO analysis was performed on this gene set andover-represented ... Clustering was performed using the K-means clustering algorithm. GO analysis wasperformed on this gene set and over-represented ... Clustering was performed using the K-means clustering algorithm. GO analysis was performed on this gene set andover-represented ... Clustering was performed using the K-means clustering algorithm. GO analysis wasperformed on this gene set and over-represented ...http://www.patentgenius.com/patent/8252761.html
Formal concept analysis - Wikipedia
... one to use standard definitions and algorithms from multidimensional concept analysis for computing multidimensional clusters. ... where n-dimensional clusters of similar values in n-dimensional data are represented by n+1-dimensional concepts. This ... Association rule learning Cluster analysis Commonsense reasoning Conceptual clustering Concept learning Correspondence analysis ... The formal concept analysis can be used as a qualitative method for data analysis. Since the early beginnings of FBA in the ...https://en.wikipedia.org/wiki/Formal_concept_analysis
Stability-based validation of dietary patterns obtained by cluster analysis | Nutrition Journal | Full Text
A clustering algorithm A with a predefined number of cluster k constructs a solution Y of the data set X into k clusters (Y := ... Ak(X)). This solution Y is represented by an n-dimensional vector of labels Y = (Y1, …, Yn) where Yi = v if the i-th individual ... Formalization of cluster analysis. Let X = (X1, …, Xn) be the dataset of n = 2298 individuals to be clustered where Xi is a 45- ... Hennig C. Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis. 52(1):258-71Google Scholar. ...https://nutritionj.biomedcentral.com/articles/10.1186/s12937-017-0226-9
Comprehensive Gene Expression Analysis of Prostate Cancer Reveals Distinct Transcriptional Programs Associated with Metastatic...
Analyses of the gene/EST clusters queried by the U95A array are described in detail below. Identical algorithms were applied to ... our analysis identified hundreds of poorly characterized EST clusters that likely represent novel genes of unknown function ... Eisen M. B., Spellman P. T., Brown P. O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. ... In the other published analysis of metastatic tumors (12) , the hierarchical clustering of cDNA array data was used to identify ...http://cancerres.aacrjournals.org/content/62/15/4499.full
Bootstrap error-adjusted single-sample technique - Wikipedia
A quantitative approach involves BEST along with a nonparametric cluster analysis algorithm. Multidimensional standard ... It is based on estimating a probability distribution representing what can be expected from valid samples. This is done use a ... Valid (unaltered) samples are defined as those that fall inside the cluster of training-set points when the BEST is trained ... False (tampered) samples are those that fall outside of the same cluster. Methods such as ICP-AES require capsules[ ...https://en.wikipedia.org/wiki/Bootstrap_error-adjusted_single-sample_technique
The Range of Neurological Complications in Chikungunya Fever | SpringerLink
Only A and B cases were considered for further analysis. After general analysis, cases were clustered according to geospatial ... All cases found were assessed using a 5-step clinical diagnostic algorithm assigning categories A-C, category A representing ... Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097. ... TC was involved in methods, systematic review, data extraction, statistical analysis, results, and discussion. MS was involved ...https://link.springer.com/article/10.1007%2Fs12028-017-0413-8
MTO 21.1: Horn, Changing Use of Modes
The output of a cluster analysis is a dendrogram-a tree-structure that represents the clusters evident in the data. Cluster ... and the squared distances can be used as input to a cluster analysis algorithm. ... Cluster Analysis. [4.4] In order to carry out a cluster analysis, we needed to produce a series of measures that could be used ... Specifically, in this study, we employ exploratory data analysis methods-notably cluster analysis-as a tool for attempting to ...http://www.mtosmt.org/issues/mto.15.21.1/mto.15.21.1.horn_huron.html
Cluster analysis - Wikipedia
... the k-means algorithm represents each cluster by a single mean vector. Distribution models: clusters are modeled using ... Since algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster ... Subspace models: in biclustering (also known as co-clustering or two-mode-clustering), clusters are modeled with both cluster ... since linkage clustering does not have a notion of "noise". In centroid-based clustering, clusters are represented by a central ...https://en.wikipedia.org/wiki/Cluster_analysis
Haplotypic analyses of the IGF2-INS-TH gene cluster in relation to cardiovascular risk traits - UCL Discovery
Different algorithms and subsets of the data were analysed using the programmes haplotype trend regression, haplo.score, ... Ten haplotypes designated in frequency order *1(20.0%) to *10(3.4%) represented 89% of all haplotypes. Haplotype *5 protected ... Haplotypic analyses of the IGF2-INS-TH gene cluster in relation to cardiovascular risk traits. HUM MOL GENET , 13 (7) 715 - 725 ... Haplotypic analyses of the IGF2-INS-TH gene cluster in relation to cardiovascular risk traits ...http://discovery.ucl.ac.uk/136231/
Collaborative filtering: People who bought this also bought... - Recommending Products | Coursera
Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as ... Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate ... In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these ...https://www.coursera.org/learn/ml-foundations/lecture/CdmdR/collaborative-filtering-people-who-bought-this-also-bought