Pith Number

pith:PCECLMH6

pith:2026:PCECLMH6ZFNBLTQ7HY77MOKVDB

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StateXDiff: Cell State-Contextualized Multimodal Diffusion for Single-Cell Perturbation Prediction

Jianzhong Jeff Xi, Ningfeng Que, Peiting Shi, Xianzhe Huang, Xiaofei Wang

StateXDiff predicts single-cell drug responses more accurately under out-of-distribution conditions by integrating transcriptomic and inferred protein features through conditional diffusion.

arxiv:2605.16104 v1 · 2026-05-15 · q-bio.GN · q-bio.QM

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

StateXDiff consistently enhances generalization performance across three challenging settings: unseen cell lines, unseen drugs, and combinatorial perturbations.

C2weakest assumption

That the inferred protein features, when combined with transcriptomic profiles, produce a disentangled representation that captures genuine biological state transitions rather than spurious correlations induced by conditional distribution shifts or low signal-to-noise ratios.

C3one line summary

StateXDiff integrates transcriptomic profiles with inferred protein features via a conditional diffusion model and mechanism-aware drug templates to predict single-cell drug perturbation responses under unseen cell lines, drugs, and combinatorial settings.

References

28 extracted · 28 resolved · 2 Pith anchors

[1] How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell, 187(25):7045–7063, 2024 2024

[2] Digital twins in oncology: where we are and where we hope to go.BMJ oncology, 4(1):e000893, 2025 2025

[3] Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.Nature Methods, 22(8):1657–1661, 2025 2025

[4] A next generation connectivity map: L1000 platform and the first 1,000,000 profiles 2017

[5] A pre-trained large generative model for translating single-cell transcriptomes to proteomes.Nature Biomedical Engineering, pages 1–20, 2025 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:52.844408Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

788825b0fec95a15ce1f3e3ff63955184d1a166a4416f57cac518364f77926fb

Aliases

arxiv: 2605.16104 · arxiv_version: 2605.16104v1 · doi: 10.48550/arxiv.2605.16104 · pith_short_12: PCECLMH6ZFNB · pith_short_16: PCECLMH6ZFNBLTQ7 · pith_short_8: PCECLMH6

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PCECLMH6ZFNBLTQ7HY77MOKVDB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 788825b0fec95a15ce1f3e3ff63955184d1a166a4416f57cac518364f77926fb

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0ccc779287fa8f5f6b3bd9e1a21eae1bf189885f50645e21e3e912e6c0cb2c90",
    "cross_cats_sorted": [
      "q-bio.QM"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "q-bio.GN",
    "submitted_at": "2026-05-15T15:54:46Z",
    "title_canon_sha256": "b1747cdae1c3e8838010daa4a21d857ca5bfbfed704b16c22573394f37207429"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16104",
    "kind": "arxiv",
    "version": 1
  }
}