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Updated: Jul 16, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

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u-LINNDA:一种通过神经网络检测辅助工具进行用户优化的淋巴瘤识别的协议.

Maximilian Fischer1, Miriam Cindy Maurer2, Robin Peretzke3

  • 1Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany; Forschungscampus M(2)OLIE, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.

STAR protocols
|November 6, 2025
PubMed
概括

这项研究介绍了u-LINNDA,一种新的卷积神经网络 (CNN) 算法,用于从脑瘤活检中诊断初级中枢神经系统淋巴瘤 (PCNSL). 该工具有助于准确地鉴定针对性治疗策略的组织病理学特征.

关键词:
生物信息学是一种生物信息学.癌症 癌症 癌症 癌症计算机科学 计算机科学健康科学 卫生科学 卫生科学神经科学是一个神经科学.

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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...