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In clustering-based techniques, a cluster of small size can be considered as clustered outliers [26].
Significant time savings are achieved in computing finite difference-based sensitivity during the optimization by employing a distributed computing techniques over a cluster of computers.
Moreover, although the CpG positions assayed with the two methods are not exactly the same due to technical constrains (Additional file 1 and Additional file 3: Figure S4), still the analysis of methylation data collected with each one of the two different techniques highlighted a cluster of CpGs with correlated methylation levels within exon 1 of ZBTB12, hypomethylated in MI cases vs controls.
This result was extended to analyze the stress concentration around a cluster of broken fibers by a superposition technique.
Chiang et al. (2006) developed a text-mining-based gene information summarization technique for a cluster of genes based on information extracted from PubMed abstracts followed by processing using a Finite State Automaton.
In the first phase, the technique provides a clustering of the nodes into m equivalence class (C1,C2,…,CM) such that each node is indistinguishable in its quasi-identifying attributes from K-1 other nodes.
As a tool, this cluster of examination techniques may be used by physicians to better understand their patients' conditions and descriptively communicate their findings.
The authors of [20] proposed a cluster formation technique where nodes use the affinity propagation (AP) method to pass messages to one another.
We used a cluster bootstrapping technique for the calculation of 95% confidence intervals (95% CI) [ 18]. 2. We used the generalized estimating equations (GEE) approach to fit logistic regression models [ 19] for evaluation and comparison of SAR between intervention groups.
We estimated 95% CIs of proportions by using a cluster bootstrap technique with 1000 resamples [13].
A cluster series expansion technique for spectral solution of vibration problems is presented.
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