https://github.com/deib-geco/multi-star
Multi-label subtyping and advanced recognition on cancer patients
https://github.com/deib-geco/multi-star
Last synced: about 12 hours ago
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Multi-label subtyping and advanced recognition on cancer patients
- Host: GitHub
- URL: https://github.com/deib-geco/multi-star
- Owner: DEIB-GECO
- Created: 2023-12-01T17:58:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-12T15:12:50.000Z (9 months ago)
- Last Synced: 2025-01-24T13:34:42.377Z (9 months ago)
- Language: Jupyter Notebook
- Size: 3.04 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# MULTI-STAR
MULTI-label SubTyping and Advanced Recognition of patient primary and secondary assignments. 'A novel Machine Learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification'.
## Description
We developed and offer a computational workflow, named MULTI-STAR, to address current limitations in state-of-the-art similarity-based stratification approaches.
MULTI-STAR indeed provides machine learning-based multi-label classifiers for more comprehensive and reliable single-sample stratification.
## Steps of the workflow
For any stratification/subtyping task at hand, MULTI-STAR workflow:
- defines a comprehensive multi-label reference by extending a similarity-based subtyping method at the state-of-the-art;
- finds a valid multi-label classifier for single-sample subtyping, evaluating several machine learning models and strategies to identify the most suitable one(s).
In the here provided notebooks, we show how to extend state-of-the-art similarity-based techniques to generate multi-label characterizations of patients, given their expression profiles.
Then, we explore alternative base learners for single-label subtyping. The best-performing base learners are combined with several problem transformation strategies
and compared with known adapted algorithms, to obtain multi-label patient subtyping.
Each multi-label classifier is optimized, trained and finally assessed on test samples using many distinct performance measures.
This wide analysis enables us to find the most promising multi-label classification approaches,
which can be further evaluated based on relevant clinical properties like the prognostic capability of specific classes.
## Application use cases
The effectiveness of the MULTI-STAR approach was validated and showcased through the development of multi-label classifiers for breast and colorectal cancer subtyping.
So-obtained classifiers offered not only superior performance but also more comprehensive insights into patient heterogeneity, even surpassing existing methods in terms of prognostic value of predictions and single-sample usability, as required by clinical practice.