Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery Article Swipe
Christophe Poulet
,
Ahmed Debit
,
Claire Josse
,
Guy Jérusalem
,
Chloé‐Agathe Azencott
,
Vincent Bours
,
Kristel Van Steen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.29.534695
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.29.534695
Biomarker signature discovery remains the main path to develop clinical diagnostic tools when the biological knowledge on a pathology is weak. Shortest signatures are often preferred to reduce the cost of the diagnostic. The ability to find the best and shortest signature relies on the robustness of the models that can be built on such set of molecules. The classification algorithm that will be used is selected based on the average performance of its models, often expressed via the average AUC. However, it is not garanteed that an algorithm with a large AUC distribution will keep a stable performance when facing data. Here, we propose two AUC-derived hyper-stability scores, the HRS and the HSS, as complementary metrics to the average AUC, that should bring confidence in the choice for the best classification algorithm. To emphasize the importance of these scores, we compared 15 different Random Forests implementation. Additionally, the modelization time of each implementation was computed to further help deciding the best strategy. Our findings show that the Random Forest implementation should be chosen according to the data at hand and the classification question being evaluated. No Random Forest implementation can be used universally for any classification and on any dataset. Each of them should be tested for both their average AUC performance and AUC-derived stability, prior to analysis. Author summary To better measure the performance of a Machine Learning (ML) implementation, we introduce a new metric, the AUC hyper-stability, to be used in parallel with the average AUC. This AUC hyper-stability is able to discriminate ML implementations that show the same AUC performance. This metric can therefore help researchers in choosing the best ML method to get stable short predictive biomarker signatures. More specifically, we advocate a tradeoff between the average AUC performance, the hyper-stability scores, and the modeling time.
Related Topics
Concepts
Random forest
Robustness (evolution)
Computer science
Metric (unit)
Stability (learning theory)
Biomarker discovery
Data mining
Artificial intelligence
Performance metric
Machine learning
Signature (topology)
Pattern recognition (psychology)
Mathematics
Engineering
Geometry
Chemistry
Biochemistry
Gene
Economics
Operations management
Management
Proteomics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.03.29.534695
- https://www.biorxiv.org/content/biorxiv/early/2023/04/01/2023.03.29.534695.full.pdf
- OA Status
- green
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362450320
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362450320Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.03.29.534695Digital Object Identifier
- Title
-
Assessing Random Forest self-reproducibility for optimal short biomarker signature discoveryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-01Full publication date if available
- Authors
-
Christophe Poulet, Ahmed Debit, Claire Josse, Guy Jérusalem, Chloé‐Agathe Azencott, Vincent Bours, Kristel Van SteenList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.03.29.534695Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/04/01/2023.03.29.534695.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/04/01/2023.03.29.534695.full.pdfDirect OA link when available
- Concepts
-
Random forest, Robustness (evolution), Computer science, Metric (unit), Stability (learning theory), Biomarker discovery, Data mining, Artificial intelligence, Performance metric, Machine learning, Signature (topology), Pattern recognition (psychology), Mathematics, Engineering, Geometry, Chemistry, Biochemistry, Gene, Economics, Operations management, Management, ProteomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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