Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 26 Jan 2023
Reviewer:  Abedalrhman Alkhateeb
Reviewer's report:
The author proposed 5-gene panel (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3) as
prognostics biomarkers for pancreatic cancer. The work is based on classification and regression
models, it is comprehensive,  evaluated by KM survival and AUCROC, and may gain so many
interests. However, I have minor suggestions:
- The literature is missing similar methods such as (PMID: 35845587) and comparison with it,
machine learning prognostics models (PMID: 35205681 and/or PMID: 30890858).
- The parameter of the regression and classification could be added ( if available).
- The authors may conduct a pathway analysis using KEGG or GO enrichment analysis tools.
Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 26 Jan 2023
Reviewer:  Abedalrhman Alkhateeb
Reviewer's report:
The author proposed 5-gene panel (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3) as
prognostics biomarkers for pancreatic cancer. The work is based on classification and regression
models, it is comprehensive,  evaluated by KM survival and AUCROC, and may gain so many
interests. However, I have minor suggestions:
- The literature is missing similar methods such as (PMID: 35845587) and comparison with it,
machine learning prognostics models (PMID: 35205681 and/or PMID: 30890858).
- The parameter of the regression and classification could be added ( if available).
- The authors may conduct a pathway analysis using KEGG or GO enrichment analysis tools.
Please review this paper.
user
Title: Prognostic Assessment Capability of a Five-Gene Signature in Pancreatic Cancer: A
Machine Learning Based-Study
Version: 2 Date: 26 Jan 2023
Reviewer:  Abedalrhman Alkhateeb
Reviewer's report:
The author proposed 5-gene panel (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3) as
prognostics biomarkers for pancreatic cancer. The work is based on classification and regression
models, it is comprehensive,  evaluated by KM survival and AUCROC, and may gain so many
interests. However, I have minor suggestions:
- The literature is missing similar methods such as (PMID: 35845587) and comparison with it,
machine learning prognostics models (PMID: 35205681 and/or PMID: 30890858).
- The parameter of the regression and classification could be added ( if available).
- The authors may conduct a pathway analysis using KEGG or GO enrichment analysis tools.
assistant