Title: Improved diagnostic efficiency of CRC subgroups revealed using machine learning based
on intestinal microbes
Version: 1 Date: 25 May 2024
Reviewer's report:
This paper reanalyzed previously published data on CRC and identified two microbiome-based
CRC subgroups using the PAM clustering algorithm. However, the authors did not clearly
explain the differences between the two subgroups. What factors are responsible for these
differences? Are they caused by different eating habits, tumor locations, disease stages, or patient
ages and genders? What are the clinical significances of these differences for the treatment
and diagnosis of CRC? The authors should conduct more in-depth research to answer these
questions. Researchers may be able to use technical means to analyze the intestinal flora and
divide CRC into more subgroups. However, if the reasons for the formation of these subgroups
and their clinical significance cannot be explained, such research lacks value.
Title: Improved diagnostic efficiency of CRC subgroups revealed using machine learning based
on intestinal microbes
Version: 1 Date: 25 May 2024
Reviewer's report:
This paper reanalyzed previously published data on CRC and identified two microbiome-based
CRC subgroups using the PAM clustering algorithm. However, the authors did not clearly
explain the differences between the two subgroups. What factors are responsible for these
differences? Are they caused by different eating habits, tumor locations, disease stages, or patient
ages and genders? What are the clinical significances of these differences for the treatment
and diagnosis of CRC? The authors should conduct more in-depth research to answer these
questions. Researchers may be able to use technical means to analyze the intestinal flora and
divide CRC into more subgroups. However, if the reasons for the formation of these subgroups
and their clinical significance cannot be explained, such research lacks value.
Please review this paper.
user
Title: Improved diagnostic efficiency of CRC subgroups revealed using machine learning based
on intestinal microbes
Version: 1 Date: 25 May 2024
Reviewer's report:
This paper reanalyzed previously published data on CRC and identified two microbiome-based
CRC subgroups using the PAM clustering algorithm. However, the authors did not clearly
explain the differences between the two subgroups. What factors are responsible for these
differences? Are they caused by different eating habits, tumor locations, disease stages, or patient
ages and genders? What are the clinical significances of these differences for the treatment
and diagnosis of CRC? The authors should conduct more in-depth research to answer these
questions. Researchers may be able to use technical means to analyze the intestinal flora and
divide CRC into more subgroups. However, if the reasons for the formation of these subgroups
and their clinical significance cannot be explained, such research lacks value.
assistant