Al Jawad Project review

1 Overview

Title of project:

Identifying Key Metadata Predictors of Salmonella AMR Genotypes through Machine Learning

Name of project author(s):

Marco Reina

Name of project reviewer:

Saraa Al Jawad

2 Instructions

Write your comments and feedback below for each section/component of the project. The goal should be to help the author improve their project. Make comments as constructive and actionable as possible. You can provide both criticism and praise.

For each component, pick one summary statement by deleting the ones that do not apply and keeping only the one that you think most closely summarizes a given component.

Make sure your final document compiles/renders into a readable, well-formatted html document.

Delete any sections/text of this template that are not part of your final review document. (Including these instructions.)

3 Specific project content evaluation

Evaluate the different parts of the project by filling in the sections below.

3.1 Background, Context and Motivation

How well is the context of the project described? Is a comprehensive background, including summary of previous/related work given? Is the project well placed into the context of existing work (including proper referencing of existing work). Is it clear why the project was undertaken and what new information it hopes to provide?

3.1.1 Feedback and Comments

The background is comprehensive and demonstrates strong subject knowledge, particularly in explaining antimicrobial resistance mechanisms and their relevance in Salmonella. The manuscript does a good job connecting surveillance systems to the availability of large-scale metadata.

3.1.2 Summary assessment (PICK ONE, DELETE THE OTHERS)

• strong contextualization and motivation

3.2 Question description

How well and clear are the question(s)/hypotheses the project aims to address described? Is it clear how the questions relate to the data?

3.2.1 Feedback and Comments

The question is clear, but it is not clear what level of performance is considered good enough for the model to be useful.

3.2.2 Summary assessment

• question/hypotheses somewhat explained

3.3 Data description

How well is the data overall described? Is the source provided? Is a codebook or other meta-information available that makes it clear what the data is?

3.3.1 Feedback and Comments

The dataset is well described. The explanation of how the binary AMR outcome is constructed is clear and appropriate.

3.3.2 Summary assessment

• source and overall structure of data well explained

3.4 Data wrangling and exploratory analysis

How well is the data cleaned/processed and explored? Are all steps reasonable and well explained? Are alternatives discussed and considered? Are meaningful exploratory results shown (e.g. in the supplementary materials)?

3.4.1 Feedback and Comments

The preprocessing steps are logical and well structured. The filtering and cleaning decisions are reasonable and aligned with the study goals.

3.4.2 Summary assessment

• essentially no weaknesses in wrangling and exploratory component

3.5 Appropriateness of Analysis

Were the analysis methods appropriate for the data? Was the analysis done properly? Were different components of the analysis (e.g. performance measure, variable selection, data pre-processing, model evaluation) done in the best way possible and explained well?

3.5.1 Feedback and Comments

The choice of Random Forest is appropriate given the categorical nature of the predictors and the potential for nonlinear relationships. The evaluation approach (cross-validation + test set) is strong and demonstrates good modeling practice. The analysis could be improved by comparing it with other modeles such as logistic regression or LASSO. This would help show whether the machine learning model provides better performance compared to a baseline method.

3.5.2 Summary assessment

• strong and reasonable analysis

3.6 Presentation

How well are results presented? Are tables and figures easy to read and understand? Are the main figures/tables publication level quality?

3.6.1 Feedback and Comments

The presentation is clear and well organized. Figures are readable and support the text effectively. One strength is the logical flow from EDA → modeling → evaluation, which makes the manuscript easy to follow.

3.6.2 Summary assessment

• results are very well presented

3.7 Discussion/Conclusions

Are the study findings properly discussed? Are strengths and limitations acknowledged? Are findings interpreted properly?

3.7.1 Feedback and Comments

The discussion appropriately interprets the results and acknowledges key limitations.

3.7.2 Summary assessment

• strong, complete and clear discussion

3.8 Further comments

The project is very well done overall. The analysis and results are clearly explained and easy to follow. I also like how you presented the project as a web-based format, which makes it easy to navigate and explore.

4 Overall project content evaluation

Evaluate overall features of the project by filling in the sections below.

4.1 Structure

Is the project well structured? Are files in well labeled folders? Do files have reasonable names? Are all “junk” files not needed for analysis/reproduction removed? By just looking at files and folders, can you get an idea of how things fit together?

4.1.1 Feedback and Comments

The repository is well organized, with clearly labeled folders (code, data, results, manuscript). The workflow is easy to follow, and the numbering of scripts improves usability. The structure makes it easy to understand how different components connect.

4.1.2 Summary assessment

• well structured

4.2 Documentation

How well is the project documented? Are you able to understand each step of the whole analysis, each decision that was made, and each line of code? Is enough information provided as comments in code or as part of Rmd files?

4.2.1 Feedback and Comments

The documentation is clear and easy to follow. The workflow is easy to understand, and I was able to follow the steps without difficulty. Adding a bit more detail in the README file could make it even clearer for new users.

4.2.2 Summary assessment

• fully and well documented

4.3 Reproducibility

Are all results fully reproducible? Is documentation provided which clearly explains how to reproduce things, and does it work without the need for any manual intervention? Are you able to re-run the whole analysis without having to do manual interventions/edits?

4.3.1 Feedback and Comments

The project appears reproducible, with clear structure and use of consistent tools. I did not encounter issues when reviewing the workflow.

4.3.2 Summary assessment

• fully reproducible without issues

4.4 Thoroughness

How thorough was the overall study? Were alternatives (e.g. different ways of processing the data or different models) considered? Were alternatives discussed? Were the questions/hypotheses fully and thoroughly addressed?

4.4.1 Feedback and Comments

The study is thorough in terms of data processing and modeling workflow.

4.4.2 Summary assessment

• strong level of thorougness

4.5 Further comments

This is a strong and well-structured project that demonstrates a solid understanding of both the biological context and machine learning workflow. Great job!