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MT-LLE: Multi-Task Locally Linear Embedding for Interpretable Disease Modeling from Longitudinal Omics Data

2026-08-27

Abstract excerpt

Constructing interpretable disease models from longitudinal omics data is a central challenge in precision medicine. The goal is a low-dimensional representation in which a patient's position encodes their molecular state and clinical severity, and along which disease progression can be read directly. Existing dimensionality reduction methods (e.g., UMAP, Variational Autoencoders) fall short of this goal: they opt...

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Literature Corpus work: f0099744-af0c-5951-ae4d-6a0313bce83e
DOI: 10.64898/2026.08.24.746083

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