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Found 6 papers, 4 papers with code
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis
The success of machine learning models relies heavily on effectively representing high-dimensional data.
Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2
By leveraging the stratification by time in sequence data, our method enables the high-resolution longitudinal analysis of topological signals of adaptation.
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
Comparing and aligning large datasets is a pervasive problem occurring across many different knowledge domains.
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
The Holdout Randomization Test for Feature Selection in Black Box Models
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
Black Box FDR
BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis.
