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PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction — arXiv:2608.21349

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21349
License (as archived): CC0-1.0
Archived: 2026-10-18T10:31:41.381Z
SHA-256 of the archived text: 73960196ab03d2375ab4f56ae33ad15fa48aeacc339a565895c2ed1fcb2400a2
Stable URL of this page: https://metraipolis.com/documento/dc_bb23639bc330

How to cite

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction — arXiv:2608.21349 — arXiv (en), https://arxiv.org/abs/2608.21349 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb23639bc330 (Metraipolis archive dc_bb23639bc330, sha256 73960196ab03d2375ab4f56ae33ad15fa48aeacc339a565895c2ed1fcb2400a2)

BibTeX

@misc{dcbb23639bc330,
  title = {PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction — arXiv:2608.21349},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21349},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb23639bc330; sha256 73960196ab03d2375ab4f56ae33ad15fa48aeacc339a565895c2ed1fcb2400a2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21349. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-24 in cs.LG).
Categorie arXiv: Quantitative Methods (q-bio.QM) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.21349 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T10:27:56.012Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).