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By GlioState Editorial Team · Published · Last updated

AI Virtual Cell — Baseline Knowledge Base · First-Round Review

Generated 2026-10-12 (PT) | 139 items (current count), see the Library; all papers verified against Europe PMC / Crossref, web items verified against official pages. English version faithfully translated from the Chinese original.

1. Definition and milestones

2. Key teams and models

3. Progress in brain tumors

4. Critiques and benchmarks

Deep models do not outperform linear baselines VC0048; foundation models underperform simple baselines on perturbation prediction VC0049; zero-shot performance is limited VC0050; PertEval-scFM VC0051; Systema shows that systematic variation leads to overestimation VC0052; evaluation principles VC0053; the gap between models and baselines persists VC0054; attention weights do not provide gene-regulatory-network information VC0056. The 2025 Challenge exposed that its metrics could be "gamed", and in 2026 the format changed to zero-shot VC0105, VC0058. On the other hand, X-Cell reports scaling laws for perturbation prediction and claims to surpass linear baselines (not yet peer reviewed) VC0033, VC0110.

5. Conference calendar (2026–2027)

See the Conferences page for the full, data-driven list. Past meetings: ICLR LMRL (2026-04) VC0116, ICML FM4LS (2026-07) VC0117, ISMB 2026 VC0128, EANO 2026 (Rome) VC0123. Dates for RECOMB / ISMB 2027 are not yet confirmed.

6. Research outlook and gaps (brain tumors)

  1. Lack of brain-tumor perturbation data: Perturb-seq has mainly been run in cell lines such as K562 and RPE1; there are currently no large-scale Perturb-seq or drug-perturbation data in GSC, organoid or brain-slice systems (Tahoe-100M includes only a few glioma cell lines — to be re-checked).
  2. "State transitions" are a natural modeling task: whether transitions among NPC/OPC/AC/MES states, and state drift after radiochemotherapy or TTFields, can be predicted could be tested with GBmap plus longitudinal samples VC0069.
  3. Spatial and TME dimensions: the glioma infiltrative margin and neuron–tumor synapses have not yet entered virtual cell models.
  4. Cross-modal bridging: use paired samples of MRI/pathology foundation models and spatial transcriptomics to build practical "patient digital twins".
  5. Evaluation: linear baselines and zero-shot controls are essential (see VC0048, VC0052).
  6. Gap area: spinal cord tumors (intramedullary ependymoma, astrocytoma) have almost no single-cell atlases, let alone virtual cell studies — a distinctive niche for this hub.

7. Site plan

Sections: ① weekly digest (auto-updated, de-duplicated by seen IDs); ② model library (cards: task, data, open-source or not, benchmark performance); ③ glioma state-atlas navigator (Neftel states, GBmap, CELLxGENE entry); ④ benchmark & critique tracker; ⑤ conference calendar; ⑥ clinician's perspective column (bilingual).

First article topics:

  1. What is an AI virtual cell? A close reading of Bunne 2024 VC0001;
  2. Why do linear baselines keep beating deep models? VC0048, VC0049;
  3. The four GBM states and plasticity: a natural target for virtual cells VC0059, VC0066;
  4. In vivo Perturb-seq comes to glioma VC0070;
  5. Two years of the Arc Challenge and how teams from China performed VC0105, VC0058;
  6. From the "virtual brain tumor" to digital twins: twenty years of mechanistic models VC0075, VC0073, VC0071;
  7. Xaira vs. Noetik: cell-first or tissue-first? VC0108, VC0111;
  8. A survey of single-cell atlases of brain metastases and meningioma;
  9. What data can neurosurgeons contribute? (intraoperative margin sampling, longitudinal recurrence samples);
  10. A round-up of virtual-cell and AI abstracts at SNO 2026.

Frequently asked questions

Which models predict cellular responses to perturbations?

Examples include GEARS, CPA and Arc Institute’s State model, often built on cell embeddings from single-cell foundation models such as scGPT and Geneformer. Reported performance varies by dataset and context. Independent benchmarks should be checked before relying on any single model. VC0013 VC0014 VC0019 VC0008 VC0009

What is the Virtual Cell Challenge?

It is a public benchmark competition launched by the Arc Institute, described as a “Turing test” for virtual cells. The 2026 edition focuses on zero-shot generalization of perturbation prediction across cellular contexts. VC0002 VC0058

Do deep learning models beat simple baselines for perturbation prediction?

Not reliably, according to published benchmarks. A Nature Methods study reported that deep-learning gene perturbation prediction does not yet outperform simple linear baselines, and a benchmark of foundation cell models reached related conclusions. Claims of progress should therefore be compared against such baselines. VC0048 VC0049