Back to search

Article

Learning proteomic disease trajectories with flow matching

2026-07-13

Abstract excerpt

High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in w...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
7a739b85-2b1f-5d45-8a5c-891c23d435d6
DOI
10.64898/2026.07.08.737311
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Learning proteomic disease trajectories with flow matchingDOI 10.64898/2026.07.08.737311
Temporal Biodynamics: An AI Platform for Identification of Stage-Relevant Targets and BiomarkersSemantic proximity
GOTFlow: Learning Directed Population Transitions from Cross-Sectional Biomedical Data with Optimal TransportSemantic proximity
FlowTransOP: Distributional Translation of Omics Signatures via Constrained Deep Flow MatchingSemantic proximity
TRIO-AI: Hybrid temporal graph, ODE, and VAE modeling for high-resolution cellular trajectory inference in liver injurySemantic proximity
Experimental Time Points Guided Transcriptomic Velocity InferenceSemantic proximity
Reconstructing True 3D Spatial Omics at Single-Cell ResolutionSemantic proximity
ProMeta: A meta-learning framework for robust disease diagnosis and prediction from plasma proteomicsSemantic proximity
Identification of Patient Trajectories in Timeseries Clinical Transcriptomics DataSemantic proximity
DTPSP: A Deep Learning Framework for Optimized Time Point Selection in Time-Series Single-Cell StudiesSemantic proximity
Deep representation learning for clustering longitudinal survival data from electronic health recordsSemantic proximity
Deep Representation Learning for Temporal Inference in Cancer Omics: A Systematic ReviewSemantic proximity
Deep representation learning for clustering longitudinal survival data from electronic health recordsSemantic proximity
Select a neighboring publication to make it the new centre.