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Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profili...

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Principais autores: Namrata Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas, Anunay Yadav, Chitrita Goswami, Smriti Chawla, Pierre Solomon, Cynthia Fourgeux, Gaurav Ahuja, Brett Hollier, Himanshu Kumar, Antoine Roquilly, Jeremie Poschmann, Melanie Lehman, Colleen C Nelson, Debarka Sengupta
Format: Artigo
Sprog:Inglês
Udgivet: eLife Sciences Publications Ltd 2025-06-01
Serier:eLife
Fag:
single-cell RNA-seq
risk stratification
transfer learning
prostate cancer
marker-free
language model
Online adgang:https://elifesciences.org/articles/98469
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