BioVectorField: Learning Transcriptomic Vector Fields from Irregularly Sampled scRNA-seq to Map Deterioration–Recovery Dynamics 


Vol. 15,  No. 2, pp. 180-187, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.180


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  Abstract

We present BioVectorField (BioVF), a framework that learns gene-expression dynamics underlying disease progression from irregularly sampled single-cell RNA-seq time-series data. For each patient, single-cell profiles were aggregated into cell-type–specific pseudo-bulk representations at each timepoint, projected into a shared PCA space, and linked across consecutive intervals to extract temporal changes. BioVF models these changes to reconstruct a continuous transcriptional vector field and visualizes latent biological trajectories spanning the deterioration-to-recovery continuum of disease. Because the learned field encodes the directionality of temporal gene-expression changes, it enables the construction of quantitative metrics for discriminating disease phases such as deterioration and recovery. When applied to Korean COVID-19 patient data, BioVF successfully recapitulated key features of the clinical deterioration–recovery trajectory and demonstrated predictive utility. Furthermore, GSEA revealed that genes contributing to the vector-field–based predictions were associated with coherent immune activation and resolution processes in high-dimensional transcriptional space.

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[IEEE Style]

J. Kim and K. Jo, "BioVectorField: Learning Transcriptomic Vector Fields from Irregularly Sampled scRNA-seq to Map Deterioration–Recovery Dynamics," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 180-187, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.180.

[ACM Style]

Junku Kim and Kyuri Jo. 2026. BioVectorField: Learning Transcriptomic Vector Fields from Irregularly Sampled scRNA-seq to Map Deterioration–Recovery Dynamics. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 180-187. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.180.

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