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We describe here, the different microarray technologies, their use and the different ways to express the data as genes expression patterns.
We write the deformed curve as (overline {gamma }), and we also express the data associated with (overline {gamma }) by putting —.
PCA can be used to identify the patterns from the data, and to express the data in a way that highlights their similarities and differences.
The measured values later analyzed using different temperature dependent exponential expressions and found that the Mott variable range hopping conduction model was successful to express the data.
We first express the data integration problem as a variational optimal control problem where we express the displacement field in terms of wavelet expansions and, secondly, we write the components of the displacement field in terms of wavelet coefficients.
We first express the data integration problem as a variational optimal control problem where we express the displacement field in terms of wavelet expansions and, secondly, we express the components of the displacement field in terms of wavelet coefficients.
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PCA expresses the data as a linear combination of the most significant features of a given posture.
Principal component analysis (PCA) is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
Smith (2002) comments that PCA is a way of identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences.
To re-express
We expressed the data as the mean number of positive cells±standard error of the mean (SEM).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com