noisy omics data vs noisiness of omics data

Both phrases are correct, but they convey slightly different meanings. 'Noisy omics data' refers to the data itself being noisy, while 'noisiness of omics data' refers to the quality of the data being noisy. They are comparable but used in different contexts.

Last updated: March 20, 2024 • 631 views

noisy omics data

This phrase is correct and commonly used in the context of data analysis to refer to data that contains a lot of noise.

This phrase is used to describe omics data that is characterized by a high level of noise or interference, making it challenging to analyze or interpret.

Examples:

The researchers had to preprocess the noisy omics data before conducting any analysis.
The noisy omics data required advanced filtering techniques to extract meaningful information.
Dealing with noisy omics data is a common challenge in bioinformatics.
The quality of the results was affected by the noisy omics data.
The presence of noisy omics data can lead to misleading conclusions.

Show examples from the web [+]

... to develop a new method that can be used to solve this task: the identification of minimal, yet robust fingerprints from very high-dimensional, noisy -omics data.
... fingerprints from very high-dimensional, noisy -omics data. Our method will be based on ideas from the areas of compressed sensing and machine learning.
Jun 22, 2007 ... Thus the simple averaging over replicates is not completely satisfactory especially for noisy omics data that do not have good within-replicate ...
Jun 22, 2007 ... completely satisfactory especially for noisy omics data that do not have good within-replicate correlation. Here we propose a new multivariate ...

Alternatives:

data with high noise levels in omics
omics data with significant noise
noisy data in omics studies
omics data containing a lot of noise
data with interference in omics analysis

noisiness of omics data

This phrase is correct and is used to describe the quality or characteristic of omics data being noisy.

This phrase is used to discuss the level of noise or interference present in omics data, focusing on the quality aspect rather than the data itself.

Examples:

The noisiness of omics data can impact the accuracy of downstream analyses.
Understanding the sources contributing to the noisiness of omics data is crucial for data preprocessing.
The researchers evaluated the noisiness of omics data before applying any statistical methods.
Quantifying the noisiness of omics data is essential for reliable results.
The noisiness of omics data poses a challenge for data interpretation.

Alternatives:

level of noise in omics data
quality of noisy omics data
degree of noisiness in omics data
amount of interference in omics data
characteristic of noisy omics data

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noisy omics data vs noisiness of omics data

noisy omics data

noisiness of omics data

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