Sentence examples for develop a normalization from inspiring English sources

We use them to develop a normalization formula.

In this paper, we develop a normalization method based on iterating median of M-values (IMM) for detecting the differentially expressed (DE) genes.

The importance of finding an appropriate normalization method for miRNA data in our colorectal cancer (CRC) study [ 3] motivated us to develop a normalization method that accounts for the characteristics of data, removes any artificial variations, and keeps the crucial biological information.

For instance, Zeng et al. (2013) developed a normalization technique based on distance to goal values to formulate an extension of the VIKOR approach (from visekriterijumska optimizacija i kompromisno resenje in Serbian that means multi-criteria optimization and compromise solution) for a practical case regarding healthcare management.

More recently, May and colleagues developed a Normalization Process Theory (NPT) to explain the implementation, embedding and integration of complex health interventions into everyday practice [ 42].

Having properly identified and quantified all these biases, we developed a normalization procedure which allows us to correct the data for all those biases at one time.

As the SN score possesses a range of values in each relation category (drug-protein, protein-disease, and drug-disease), we developed a normalization method using the connecting probability function of SN score distribution (see Materials and Methods for details).

Because our state variables are generally limited to values between 0 and 1, we developed a normalization scheme that constrained activating Hill functions such that f(0) = 0, f(1) = 1 and f EC50) = 0.5.

We then developed a simple normalization procedure to process the data and allow the generation of a normalized, highly contrasted, chromosomal contact map for S. cerevisiae.

In a previous study, Huber et al. (2006) developed a data normalization procedure for transcriptome tiling array analysis.

We develop a framework for supervised normalization of microarray data, which is applicable to a large class of experimental designs and technologies, including single-channel arrays, dual-channel arrays and different probe designs (e.g. one probe per gene, probe sets and exon arrays).