andreimargeloiu/WPFS

Weight Predictor Networks with Sparse Feature Selection for Small Size Tabular Biomedical Data. Published at AAAI 2023

26
/ 100
Experimental

This helps biomedical researchers and scientists analyze small, high-dimensional biological datasets where traditional methods often struggle. You provide tabular biomedical data, and the system outputs classifications or predictions based on the features, along with insights into which features are most important. It's designed for anyone working with limited patient samples or experimental data points but many potential biological markers.

No commits in the last 6 months.

Use this if you need to build predictive models from small-sample, high-dimensional tabular biomedical data and want to understand which features are driving the predictions.

Not ideal if you are working with very large datasets or non-biomedical tabular data, as it's specifically optimized for small, high-dimensional biomedical cases.

biomedical research genomics clinical trials drug discovery bioinformatics
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 12 / 25

How are scores calculated?

Stars

20

Forks

3

Language

Python

License

Last pushed

Jun 29, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/andreimargeloiu/WPFS"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.