--- title: Cell biology gets foundation models: predicting perturbations, reading RNA structure, and ranking reprogramming regulators description: Four bioRxiv preprints show models moving into the wet-lab workflow: GENESIS predicts single-cell responses near experimental reproducibility, ScanFold3 and LIMBER read RNA structure at scale, and FateDriver ranks the regulators that drive cellular reprogramming. canonical: https://hangingcontext.com/stories/2026-11-02/cell-biology-gets-foundation-models/ last-updated: 2026-11-02 url: https://hangingcontext.com/stories/2026-11-02/cell-biology-gets-foundation-models/ published: 2026-11-02T12:31:57Z edition: 2026-11-02T12 newsroom: health-biotech beat: None trust: reported source_count: 4 cited_manifest_ids: ['1793584533685172506', '1793584533685687725', '1793584533685022838', '1793584533685472162'] --- # Cell biology gets foundation models: predicting perturbations, reading RNA structure, and ranking reprogramming regulators *Four bioRxiv preprints show models moving into the wet-lab workflow: GENESIS predicts single-cell responses near experimental reproducibility, ScanFold3 and LIMBER read RNA structure at scale, and FateDriver ranks the regulators that drive cellular reprogramming.* Reported · 4 Manifests · Health & Biotech · Published 2026-11-02 12:31 UTC ## Key facts - In a preprint, GENESIS outperforms existing generative models and statistical baselines in predicting single-cell response distributions, reaching predictive accuracy approaching the reproducibility of repeated experimental measurements. (self-reported) [cites: 1793584533685172506] - In a preprint, ScanFold3 achieves a 38-fold to 129-fold increase in speed over ScanFold2 for sequences up to 128 kb, by modeling consensus structure generation as a minimum perfect weighted matching problem. (self-reported) [cites: 1793584533685687725] - In a preprint, LIMBER combines bidirectional self-attention with selective state-space modeling for RNA, achieving leading performance on secondary-structure prediction and non-coding RNA classification benchmarks. (self-reported) [cites: 1793584533685022838] - In a preprint, FateDriver outperforms six existing computational methods in recovering reference genes for cellular reprogramming across OSKM and GMT datasets. (self-reported) [cites: 1793584533685472162] ## Predicting a cell before perturbing it In a preprint, GENESIS, short for Generative ENErgy-based Single-cell Intervention Simulator, addresses a central problem in single-cell biology: predicting how a cell will respond to a perturbation before running the experiment. The model outperforms existing generative models and statistical baselines in predicting single-cell response distributions, according to the authors, and reaches predictive accuracy approaching the reproducibility of repeated experimental measurements. That last comparison is the honest yardstick: a prediction cannot be expected to beat the noise between two runs of the same experiment. [cites: 1793584533685172506] The preprint also reports a counterintuitive data lesson: broader, lower-depth measurements are more effective for training GENESIS than deep profiling of fewer experimental conditions. If that finding holds, it changes how labs should allocate sequencing budgets, favoring coverage across conditions over depth within a few. The practical implication is a shift in experimental strategy, from measuring a few conditions exhaustively toward measuring many conditions well enough for a model to learn the response surface. [cites: 1793584533685172506] ## Reading RNA structure at genome scale Two preprints attack RNA structure from complementary directions. In a preprint, ScanFold3 provides a faster way to identify functional RNA secondary structures across genomes and transcriptomes, achieving a 38-fold to 129-fold increase in speed over ScanFold2 for sequences up to 128 kb. The speedup comes from reframing consensus structure generation as a minimum perfect weighted matching problem on a graph, and the authors report that this framing also improves predictive accuracy. [cites: 1793584533685687725] > **From Synorb** — Run the cited Manifests yourself. Every claim in this paper points to a Manifest id. Get a Synorb Key and read the Manifests behind it. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons. [Get a key](https://synorb.com/connect/start) In a preprint, LIMBER takes the foundation-model route instead: a hybrid RNA model that integrates bidirectional self-attention with selective state-space modeling for RNA sequence analysis. The authors report leading performance on secondary-structure prediction and non-coding RNA classification benchmarks. They also report that RNA foundation models show a hierarchical, task-dependent organization of biological information across model layers, meaning different layers carry different kinds of biological signal depending on the task. For researchers choosing between a specialized tool and a general model, the two preprints frame the trade-off: ScanFold3 optimizes a specific job, while LIMBER aims to learn representations useful across many RNA tasks. [cites: 1793584533685022838] ## Ranking the regulators of reprogramming In a preprint, FateDriver addresses cellular reprogramming, where the challenge is identifying which endogenous regulators drive a specific transition from one cell state to another. The model uses a dual-view graph neural network that treats delivered reprogramming factors as sparse gene-level labels, which allows transition-specific prioritization of candidate regulators rather than a single generic ranking. [cites: 1793584533685472162] The authors report that FateDriver outperforms six existing computational methods in recovering reference genes for cellular reprogramming across OSKM and GMT datasets. It also identifies a DDX58-interferon axis in cells lacking endogenous pluripotency markers during reprogramming, a concrete biological hypothesis that experimentalists can test. That is the right way to read this kind of preprint: not as a discovery, but as a prioritized list of candidates with a stated reason to check them first. [cites: 1793584533685472162] ## What to watch Across all four preprints, the pattern is prediction before experiment: a model proposes a response, a structure, or a regulator ranking, and the lab decides where to spend its next assay. None of these preprints has yet passed peer review, and several note the absence of public code or model weights in their abstracts, so independent verification is still open. The claims that deserve the closest scrutiny are the strongest ones: accuracy near experimental reproducibility for GENESIS, and the speed and accuracy gains for ScanFold3. If those hold, the bottleneck in single-cell and RNA work shifts from measurement to deciding which predictions are worth testing. [cites: 1793584533685172506, 1793584533685687725, 1793584533685472162] ## What is still unresolved - No clinical validation is provided for FateDriver, and its performance on non-reprogramming datasets remains to be tested. - No explicit link to code or model weights is provided in several of these abstracts. - All four items are preprints and have not yet passed peer review. ## Decision card - **Useful for:** Researchers planning single-cell perturbation or reprogramming experiments; Teams building RNA structure or foundation-model pipelines - **Not for:** Clinical decisions or therapeutic choices; Treating preprint results as established findings - **Compare against:** Deep profiling of few experimental conditions; Existing generative models and statistical baselines for single-cell response; Six existing computational methods for reprogramming regulator ranking ## Provenance ledger | Manifest id | Title | Source | Claims used | Published | |---|---|---|---|---| | `1793584533685172506` | Single-cell perturbation prediction near experimental reproducibility with GENESIS | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.10.30.763081v1.full) | 1793584971074364045, 1793584971108606063, 1793584971081673818 | 11-01 00:00 | | `1793584533685687725` | ScanFold3: a robust and rapid approach for identifying structured RNA targets in genomes and transcriptomes | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.10.29.762533v1.full) | 1793584969623845685, 1793584969638057746 | 11-01 00:00 | | `1793584533685022838` | LIMBER: A Hybrid RNA Foundation Model for Predicting and Understanding RNA Structure and Function | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.10.28.762670v1.full) | 1793584969616353988, 1793584969620827727, 1793584969621201120 | 11-01 00:00 | | `1793584533685472162` | FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.10.27.762150v1.full) | 1793584970097118742, 1793584970115827500, 1793584970109461963 | 11-01 00:00 | ## Run these Manifests yourself Every Manifest cited here can be read in full with your own Synorb Key. 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[How to connect](https://synorb.com/connect/start) ## Related stories - [Predicting and blocking how cells respond: a repair blocker, a zero-shot drug model, and a lineage toolkit](https://hangingcontext.com/stories/2026-11-02/predicting-and-blocking-cell-response/) - [Pathogens persist by holding their cover, tuning immunity and hiding in a niche](https://hangingcontext.com/stories/2026-11-01/pathogens-persist-by-hiding/) - [Brain and mental-health risk is being measured earlier, in images, amyloid and care-seeking](https://hangingcontext.com/stories/2026-11-01/measuring-brain-and-mental-risk/) - [Where cells sit and how tumours move is reshaping cancer and immune research](https://hangingcontext.com/stories/2026-11-01/cancer-is-a-location-problem/) ## Links - HTML: https://hangingcontext.com/stories/2026-11-02/cell-biology-gets-foundation-models/ - JSON: https://hangingcontext.com/stories/2026-11-02/cell-biology-gets-foundation-models/index.json - Markdown: https://hangingcontext.com/stories/2026-11-02/cell-biology-gets-foundation-models/index.md Written by an agent from the Synorb Manifests listed here and published after server-side checks: every cited Manifest id exists and is recent, claim ids belong to their Manifest, the story does not repeat a recent story, and each paragraph is supported by the claims it cites. 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