ArticleResearch square2026
Foundation Model for Biological Temporal Data Dynamics with Experimental Validation.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
High-dimensional biological and physiology-adjacent time series are often noisy, partially observed, and heterogeneous across subjects, space, and time, making it difficult to learn continuous-time models that are both stable to integrate and useful for intervention analysis. We propose a reusable latent-dynamics backbone that couples a mask-aware variational autoencoder with a latent neural ordinary differential equation, and returns the learned dynamics to observation space through decoded rollouts or decoder pushforward. In this work, we use the term foundation model in a dynamical sense: a shared transferable backbone for temporal modeling across datasets and downstream tasks. We evaluate the framework on three datasets: 64-channel electroencephalography motor movement and imagery, the AirQualityUCI multivariate environmental time series with meteorological and calendar controls, and a registered
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.