andreimargeloiu/WPFS
Weight Predictor Networks with Sparse Feature Selection for Small Size Tabular Biomedical Data. Published at AAAI 2023
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.
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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.
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Python
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Last pushed
Jun 29, 2023
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