论著摘要 |【AI-MR】通过深度学习预测造血谱系选择(双语版)

2018-02-06 09:48:44 admin 2
标签:   人工智能 深度神经网络 造血细胞 细胞分子特性

Prospective identification of hematopoietic lineage choice by deep learning.

发表日期: 2017.02.20   来源:Nat Methods. 2017 Apr;14(4):403-406.

作者:

Buggenthin F#1, Buettner F#1,2, Hoppe PS3,4, Endele M3, Kroiss M1,5 , Strasser M1, Schwarzfischer M1, Loeffler D3,4, Kokkaliaris KD3,4, Hilsenbeck O3,4, Schroeder T3,4, Theis FJ1,5, Marr C1.

作者介绍:

1. Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.

2. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK.

3. Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, 4058 Basel, Switzerland.

4. Research Unit Stem Cell Dynamics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.

5. Department of Mathematics, Technische Universität München, 85748 Garching, Germany.

#Contributed equally

摘要

分化改变了干细胞和祖细胞的分子特性,导致其形状和运动特征的改变。我们提出了一种深度神经网络,在使用明场显微镜和细胞运动的图像补丁区分主要造血祖细胞时,前瞻性地预测谱系选择。令人惊讶的是,在传统分子标记可观察之前,谱系选择可以被检测到三代。我们的方法可以鉴定具有差异表达的谱系特异性基因的细胞而无需分子标记。

Abstact

Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.

阅读原文:PMID: 28218899  PMCID: PMC5376497  DOI: 10.1038/nmeth.4182


慧影医疗科技(北京)有限公司

地点:北京市海淀区中关村东升科技园B2-C103

电话:400-890-9020

邮箱:radcloud@huiyihuiying.com

关闭
图片
图片