Sentences with phrase «uses cluster analysis»

To obtain such parenting types, one often uses cluster analysis.

They did this using cluster analysis, which told them how often player decisions tended to cluster around a specific role.

To do this we use cluster analysis, a statistical method designed to group objects of study (in this case, schools) based on similar qualities.

A New Approach to Investigate Students» Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

The discussion then moves on to explore, using cluster analysis, the extent to which children exhibit difficulties simultaneously across multiple domains.

Due to the different behaviors of the Emotional Intelligence dimensions of attention, clarity, and repair, we then used the cluster analysis technique in order to determine whether different profiles existed in the grouping of these dimensions, as has been done in prior research (García - Férnandez et al. 2015; García - Linares et al. 2015; Gázquez et al. 2015).

Using cluster analysis to assess the effects of stressful life events: Probing the impact of parental alcoholism on child stress and substance use

Not exact matches

Between - group analysis of phase 2 demonstrated a significant activation cluster in the ipsilateral posterior insula (pIns) in group P. Using the pIns as a seed region the PPI analysis yielded a significant enhanced coupling to the midbrain (periaqueductal grey / ventral tegmental area) after analgesia onset in group A only.

Using language - analysis software they identified the creative words and grouped them into clusters.

We intend to provide a few simple portals as a starting point, but new portals can be developed by anyone in the scientific or computational community, for a wide diversity of use cases, including: clustering, differential interference, spatial reconstruction, visualization, and graph - based analysis.

To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again using cluster - robust standard errors to account for dyadic dependencies in the data.

A further semi-objective classification using hierarchical cluster analysis is in line with these classifications, supporting the metadata approach.

It should be noted, however, that the typing method used here is based on the analysis of a noncoding spacer region within the rRNA gene cluster of B. burgdorferi [16, 19, 28, 35].

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology.

The cluster dendogram was generated using a hierarchical cluster analysis in R (http://stat.ethz.ch/R-manual/R-patched/library/stats/html/hclust.html).

In small cell lung cancer, starting from bioinformatics analyses of large gene expression datasets, we clustered subsets of co-expressed gene modules, derived networks of transcription factors and simulated their dynamics using logic - based mathematical modeling.

Using dual regression and seed - based analyses, we observed significantly decreased FC of the default mode network to 2 regions in the posterior medial cortex (PMC): the posterior cingulate cortex (PCC) and the left precuneus (threshold - free cluster enhancement, family-wise error corrected P < 0.05).

Microarray hybridization patterns were interpreted using hierarchical cluster analysis as previously described [65], [66].

Since there is a higher than 95 % chance that cluster assignments are accurate (Supplemental File S2), and our validation analysis shows that 90.7 % of the array expression patterns match the RNA analysis results using other techniques (e.g., Q - PCR), we estimate that more than 86 % of the genes in a cluster follow the corresponding average expression profile.

To overcome this problem, research groups at Stanford University and the Massachusetts Institute of Technology began to use a statistical method called cluster analysis, Zhao said.

ANOVA, principal component analysis (PCA) and clustering analyses were also performed using DAnTE.

«But a major limitation of cluster analysis,» he said, «is that it doesn't use information external to the microarray data - the kinds of things that are important in solving problems of biological interest.»

We analysed these parasite genotypes for genetic structure using principal component analysis and assessed local and global clustering using statistical measures of spatial autocorrelation.

The selection criteria brought the number of transcripts used for cluster analysis to 5,959.

To understand the selection mechanism behind mutations, network - based studies were used to estimate the importance of a mutated protein compared to non-mutated ones in signalling and protein — protein interaction networks.10, 11,12,13 Proteins mutated in cancer were found having a high number of interacting partners (i.e., a high degree of connectivity), which indicates high local importance.10 Mutated proteins are also often found in the centre of the network, in key global positions, as quantified by the number of shortest paths passing through them if all proteins are connected with each other (i.e., they have high betweenness centrality; hereafter called betweenness).11, 12 Mutated proteins also have high clustering coefficients, which means their neighbours are also neighbours of each other.10, 13 Moreover, neighbourhood analysis of mutated proteins have been previously successfully used to predict novel cancer - related genes.14, 15 However, to the best of our knowledge, no study has concentrated particularly on the topological importance of first neighbours of mutated proteins in cancer, and their usefulness as drug targets themselves.

Contrasts between sexes were also found in a cluster analysis of the pro?les using their factor scores.

Teacher candidates completed photomissions related to concepts from geography, mapped the photographs using Flickr, and did basic cluster analyses to make connections between the presence of geography concepts and their placement within the local community.

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Because cluster analysis uses a combination of parameters to determine dissimilarity between groups, it is not possible to determine from the present data why these similar - sized breeds were separated.

It uses k - means clustering analysis based on playing habits and tags to divide games into groups.

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In light of the comments by Craig Loehle and Willis Eschenbach, we decided to update our original draft to include sections, • Discussing the different reconstruction methods used by the 19 proxy - based estimates, and their relative advantages / disadvantages • Providing a more detailed discussion of the lack of consistency between individual proxies, and the importance of carrying out rigorous «sensitivity studies», including a discussion of Willis Eschenbach's cluster analysis.

Various technology tools that make the concept of E-Library indispensable are Easy Access to provide a campus wide access using IP Authentication, Results clustering to familiarize new users with different classes of content by providing an instant, multi faceted analysis of distribution of hits in each result set, flexible display option with inclusion of full featured tools that allow for printing, emailing and saving, interoperability that works with systems one use to manage electronic holdings through e-journals systems, Article linking Federated search, Meta search & Citation export to Reference Works, smart indexing technology to help users to reach the information they need by applying controlled vocabulary terms for several different taxonomies and powerful source selection to identify sources by type, language, topic, geography and other facets.

I have experience as a statistical modeler and analyst developing risk models using multivariate techniques, marketing segmentation using clustering, process analysis using decision tree machine learning techniques, and time series analysis for...

Implemented proactive database monitoring like locking, deadlock analysis, long running query, clustering failover using DMVs and stored procedure which reduced database outage and improved performance

Study selection and analysis: RCTs (including cluster and crossover RCTs) were eligible if they examined the use of CBT (at least nine sessions) in children or adolescents (age 4 — 18 years) with DSM or ICD diagnosis of generalised...

Subjects» ancestries were estimated by using a set of unlinked genetic markers by Bayesian cluster analysis, using the procedures and structure software developed by Pritchard and colleagues (36 — 38).

Weighted and geographic clustering of data were taken into account in the data analyses by using a jackknife repeated replications simulation method implemented in SAS macro V. 14.

Analyses were conducted using SUDAAN V. 8.1 to adjust for clustering and weighting.

K - means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups.

We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis.

STATA version 13 was used to carry out the multiple imputation, Exploratory Factor Analysis (EFA) and the k - means cluster aAnalysis (EFA) and the k - means cluster analysisanalysis.

If there is insufficient information to control for clustering, we will enter outcome data into RevMan using individuals as the units of analysis, and then conduct a sensitivity analysis excluding such studies (Sensitivity analysis), to assess the potential biasing effects of inadequately controlled clustered trials (Donner 2001).

Analyses were implemented at the level of the individual using random effects (multilevel) linear regression models24 fitted using maximum - likelihood estimation to allow for the correlation (or clustering) between the responses of subjects from the same MCH unit.

For the interaction analysis, the average percent signal change was extracted from the significant cluster for each condition using MarsBar (Brett et al., 2002) to examine the direction of the response; following this, the SPSS 16.0 was used to conduct a simple effect analysis.

In a second study, a person - oriented approach was used to investigate girls» profiles of emotional tone in close relationships by means of cluster analysis, and to compare the clusters on measures of deliberate self - harm.

Data was analyzed using psychometrical scale analysis to identify validity and reliability, two - step cluster analysis in order to identify typological clusters of lovers, and factor analysis to understand the composition of dimensional structures.

To facilitate such analyses, researchers used a clustered sampling design based around 132 schools to recruit the nationally representative sample.

In order to correct for the deflation of standard errors and widened confidence intervals imposed by the dyad - level analysis, a cluster robust function was used to obtain individual specific confidence intervals.