---
title: "CohortFM: Reconstructing Continuous Molecular Trajectories from Sparse Longitudinal Samples"
id: "biorxiv-4-from-sparse-visits-to-continuous-molecular-trajectories-with-flow-matching"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-4-from-sparse-visits-to-continuous-molecular-trajectories-with-flow-matching"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1?rss=1"
published_at: "2026-10-10T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CohortFM: Reconstructing Continuous Molecular Trajectories from Sparse Longitudinal Samples
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-4-from-sparse-visits-to-continuous-molecular-trajectories-with-flow-matching
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1?rss=1)
- **Published At:** 2026-10-10T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Longitudinal transcriptomic studies typically provide only a few discrete molecular samples per patient, which limits understanding of patient progression and hinders comparisons when progression rates differ. - The authors introduce **CohortFM**, a flow-matching framework that reconstructs continuous, patient-specific molecular trajectories from sparse observations and groups them into cohort-level progression patterns using an integrated mixture model. - CohortFM decouples molecular progression from calendar time, placing patients on a common progression scale and using observed intermediate states to guide learned trajectories. - The framework maps inferred progression patterns back to real patient samples to support biological interpretation. - CohortFM was evaluated on datasets from COVID-19, sepsis, influenza, and tuberculosis; it generalized across differing sampling schemes and produced consistent, meaningful trajectories. - The reconstructed trajectories capture biological differences between patients not apparent from observed samples alone and provide a finer progression scale than visit-level analyses. - Compared with state-of-the-art trajectory baselines, CohortFM yields more biologically distinct trajectories and a more complete population representation. - CohortFM was tested for interpolation and forward prediction: it remained competitive with strong interpolation baselines and ranked among top learned predictors for forecasting unobserved states. - Ablation studies reported that **flow matching** is the core learning mechanism and that the authors’ design choices improve continuous trajectory learning. - The authors conclude CohortFM is a practical approach to turn sparse molecular snapshots into continuous, biologically interpretable trajectories, enabling study of disease progression and prediction of future molecular states even with few samples per patient.
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Peyman Passban 1 Sanofi; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Peyman%2BPassban%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Passban%20P&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3APeyman%2BPassban%2B) * For correspondence: pe.psbn@gmail.com Shikhar Gupta 2 University of California, Berkeley * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shikhar%2BGupta%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Gupta%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShikhar%2BGupta%2B) Bryan Guan 2 University of California, Berkeley * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Bryan%2BGuan%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Guan%20B&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ABryan%2BGuan%2B) Tanya Roosta 2 University of California, Berkeley * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Tanya%2BRoosta%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Roosta%20T&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATanya%2BRoosta%2B) * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5856550/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5856550/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5856550/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5856550/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.02.756304v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5856550/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Longitudinal transcriptomic studies often capture only a few molecular snapshots of each patient over time. This makes it challenging to understand how patients are progressing, compare them when progression rates differ, and capture molecular changes between observed visits. We introduce CohortFM, a flow-matching framework that reconstructs continuous patient-specific molecular trajectories from sparse longitudinal observations and organizes them through an integrated mixture model into a compact set of cohort-level progression patterns. CohortFM separates molecular progression from calendar time, places patients on a shared progression scale, uses observed intermediate states to guide the learned trajectories, and maps the resulting patterns back to real patient samples for biological interpretation. We evaluated CohortFM across COVID-19, sepsis, influenza, and tuberculosis. Despite different sampling patterns, the method generalizes well and consistently produces meaningful trajectories. These trajectories capture biological differences between patients that cannot be explained by the observed samples alone. Compared with state-of-the-art trajectory baselines, CohortFM produces more biologically distinct trajectories and a more complete representation of the patient population. Its finer progression scale also reveals biological programs and temporal dynamics that are not captured by visit-level analysis. We further evaluated interpolation and true forward prediction to test whether CohortFM can accurately recover unobserved states. CohortFM remains competitive with strong interpolation baselines and is among the strongest learned predictors in the harder forecasting setting. Ablation experiments further show that flow matching is the core learning mechanism and that our design choices improve continuous trajectory learning. Our results support CohortFM as a practical framework for turning sparse molecular snapshots into continuous, biologically interpretable trajectories. By going beyond visit labels, CohortFM makes it possible to study disease progression and predict future molecular states even when patients are sampled only a few times. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC 4.0 International license](http://creativecommons.org/licenses/by-nc/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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