Sentence examples for microarray data such from inspiring English sources

Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities.

Several well-known and frequently used techniques for designing classifiers from microarray data, such as a support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low comprehensibility.

With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions.

These simulations mimic the most commonly assumed models for single channel microarray data such as that provided by Affymetrix.

Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules.

Over nine different cytochrome P450 genes were detected in our microarray data, such as monooxygenase/p-coumarate 3-hydroxylase, monooxygenase 83B1 and ent-kaurenoate oxidase.

Meta-analyses of large publicly available microarray data sources, such as GeneExpressionOmnibus [7] and ArrayExpress [8] have been shown to facilitate the analysis gene expression across healthy and disease states [9], [10], [11].

In order to identify gene expression patterns in urinary BC, we further analyzed our microarray data in such a way that common patterns of expression among the different samples were identified.

Project topics were taken from Microarray Gene Expression Data (www.mged.org) and accepted public microarray data repositories such as ArrayExpress (Brazma et al., 2003).

To our knowledge, no other software that aims to perform integrated analysis of microarray data offers such a high level of interactivity.

The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias.