noisy omics data vs noisiness of omics data

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.

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

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