{"scope":{"layer":"general","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"peer_reviewed","importance":"","importance_reason":""},"id":"VC0150","type":"paper","title":"Reconstructing signaling histories of single cells via perturbation screens and transfer learning","authors_or_org":"Hutchins NT, Meziane M, Lu C, Mitalipova M, Fischer DS, Li P.","date":"2026-09-08","year":"2026","venue":"Nature Methods","url":"https://doi.org/10.1038/s41592-026-03213-8","doi":"10.1038/s41592-026-03213-8","pmid":"42711495","brain_tumor_focus":"no","tags":["signaling","transfer-learning","perturbation-atlas","stem-cells","development"],"tag_slugs":[],"tag_labels":[],"tag_labels_zh":[],"unmapped_tags":["signaling","transfer-learning","perturbation-atlas","stem-cells","development"],"one_line_summary_en":"Hutchins et al. (Nature Methods 2026): IRIS, a neural network trained on a signaling-perturbation atlas of human pluripotent stem cells, infers signaling activities and histories of in vivo cell types; applied to mouse-embryo single-cell atlases. Developmental biology, not a tumor study.","one_line_summary_zh":"Hutchins 等（Nature Methods 2026）：IRIS 是在人多能干细胞信号扰动图谱上训练的神经网络，可推断体内细胞类型的信号活性与历史；应用于小鼠胚胎单细胞图谱。属发育生物学，并非肿瘤研究。","verified":"yes","verification_source":"Crossref (title, 6 authors, 2026-09-08, Nature Methods 23(10):1973-1983) + Europe PMC (PMID 42711495, abstract); MIT News 2026-10-15 corroborates Sept 8 publication; full text not read","page_url":"https://gliostate.com/library/VC0150/","api_url":"https://gliostate.com/api/items/VC0150.json","sameAs":["https://doi.org/10.1038/s41592-026-03213-8","https://pubmed.ncbi.nlm.nih.gov/42711495/"],"is_preprint":false,"facets":{"type":"https://gliostate.com/library/type/paper/","year":"https://gliostate.com/library/year/2026/","tags":[]},"cited_by":[],"related":[]}