On triangle inequalities of correlation-based distances for gene expression profiles

Summary of the article

• This paper presents a study of the triangle inequality of correlation-based distances for gene expression profiles.
• The authors used a dataset of gene expression profiles from the Human Genome Project to compare the triangle inequality of correlation-based distances.
• The authors found that the triangle inequality of correlation-based distances was violated in some cases.
• The authors also found that the triangle inequality of correlation-based distances was more likely to be violated when the gene expression profiles were more similar.
• The authors concluded that the triangle inequality of correlation-based distances should be taken into account when analyzing gene expression profiles.

Detailed Summary of the article

This paper presents a study of the triangle inequality of correlation-based distances for gene expression profiles. The authors used a dataset of gene expression profiles from the Human Genome Project to compare the triangle inequality of correlation-based distances. The authors found that the triangle inequality of correlation-based distances was violated in some cases. The authors also found that the triangle inequality of correlation-based distances was more likely to be violated when the gene expression profiles were more similar.

The authors used a variety of correlation-based distances to compare the gene expression profiles, including the Pearson correlation coefficient, the Spearman correlation coefficient, and the Kendall correlation coefficient. The authors found that the triangle inequality of correlation-based distances was violated in some cases, particularly when the gene expression profiles were more similar. The authors also found that the triangle inequality of correlation-based distances was more likely to be violated when the gene expression profiles were more similar.

The authors concluded that the triangle inequality of correlation-based distances should be taken into account when analyzing gene expression profiles. The authors suggested that the triangle inequality of correlation-based distances should be used to identify gene expression profiles that are more likely to be similar. The authors also suggested that the triangle inequality of correlation-based distances should be used to identify gene expression profiles that are more likely to be different.

The authors also discussed the implications of their findings for the analysis of gene expression profiles. The authors suggested that the triangle inequality of correlation-based distances should be used to identify gene expression profiles that are more likely to be similar. The authors also suggested that the triangle inequality of correlation-based distances should be used to identify gene expression profiles that are more likely to be different. The authors concluded that the triangle inequality of correlation-based distances should be taken into account when analyzing gene expression profiles.

Conclusion

In conclusion, this paper presents a study of the triangle inequality of correlation-based distances for gene expression profiles. The authors used a dataset of gene expression profiles from the Human Genome Project to compare the triangle inequality of correlation-based distances. The authors found that the triangle inequality of correlation-based distances was violated in some cases, particularly when the gene expression profiles were more similar. The authors concluded that the triangle inequality of correlation-based distances should be taken into account when analyzing gene expression profiles. The authors suggested that the triangle inequality of correlation-based distances should be used to identify gene expression profiles that are more likely to be similar or different.

source of this article

published: 2023;
On triangle inequalities of correlation-based distances for gene expression profiles

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