Latent spaces for tumour transcriptomes

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL Nature Biomedical Engineering Pub Date : 2024-12-17 DOI:10.1038/s41551-024-01322-3

Adriana Ivich, Casey S. Greene

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.

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肿瘤转录组的潜伏空间

通过无监督深度学习为基因表达数据生成低维潜在空间，可以揭示癌症的生物学特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Biomedical Engineering Medicine-Medicine (miscellaneous)

CiteScore

45.30

自引率

1.10%

发文量

138

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.

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