{
  "id": 202410,
  "title": "MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics",
  "url": "https://urgent.news/2026/08/05/mega-ode-learning-biologically-structured-and-navigable-continuous",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-05T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.05.742921v1?rss=1"
  },
  "original_language": "en",
  "account": "Perturbation-omics experiments typically observe only a fraction of molecular features, interventions, and time points, resulting in missing response trajectories, perturbation effects, and transitions associated with disease or differentiation. MEGA-ODE, a graph-constrained continuous-time framework, is introduced to reconstruct sparse dynamic omics landscapes, predict unseen molecular states, and prioritize virtual perturbations towards specific biological goals. The framework combines molecular-network priors, graph neural ordinary differential equations, and context-adaptive mixture-of-experts routing.\n\nWhen tested on L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE outperformed baseline methods in predicting unseen features and novel perturbations. In SARS-CoV-2 infection time-series data, MEGA-ODE maintained competitiveness in forecasting future time points. Applying MEGA-ODE to a COVID-19 patient cohort enhanced retrospective disease-stage stratification using predicted intermediate profiles, as opposed to relying solely on observed profiles. The expert programs identified immune and inflammatory signals linked to disease severity, while graph- and expert-level attributions in MAPK drug-response and stem-cell differentiation case studies focused on MAPK edges, developmental regulators, and transcription factor-target relationships, all backed by independent promoter-proximal ChIP-seq overlap.\n\nMEGA-ODE specifically pinpointed candidate transcription-factor perturbations that could shift 12-36 hour profiles towards 96 hour definitive-endoderm marker signatures in hESC-to-definitive-endoderm differentiation. This approach framed trajectory navigation as a concrete hypothesis-generation task. Across the various case studies, MEGA-ODE demonstrated the potential of biologically structured continuous-time modeling for prediction, interpretation, and prioritization of virtual perturbations from sparse temporal omics data.",
  "summary": "Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}