{"title":"Latent spaces for tumour transcriptomes","authors":"Adriana Ivich, Casey S. Greene","doi":"10.1038/s41551-024-01322-3","DOIUrl":null,"url":null,"abstract":"Generating low-dimensional latent spaces for gene-expression data via unsupervised deep learning can unveil biological insight across cancers.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"254 1","pages":""},"PeriodicalIF":26.8000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1038/s41551-024-01322-3","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Generating low-dimensional latent spaces for gene-expression data via unsupervised deep learning can unveil biological insight across cancers.
期刊介绍:
Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.