Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, SAR, China.
Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, SAR, China.
Bioinformatics. 2018 Dec 1;34(23):4129-4130. doi: 10.1093/bioinformatics/bty524.
Tissue biopsy is commonly used in cancer diagnosis and molecular studies. However, advanced skills are required for determining cancerous status of biopsies and tissue origin of tumor for cancerous ones. Correct classification is essential for downstream experiment design and result interpretation, especially in molecular cancer studies. Methods for accurate classification of cancerous status and tissue origin for pan-cancer biopsies are thus urgently needed.
We developed a deep learning-based classifier, named GeneCT, for predicting cancerous status and tissue origin of pan-cancer biopsies. GeneCT showed high performance on pan-cancer datasets from various sources and outperformed existing tools. We believe that GeneCT can potentially facilitate cancer diagnosis, tumor origin determination and molecular cancer studies.
GeneCT is implemented in Perl/R and supported on GNU/Linux platforms. Source code, testing data and webserver are freely available at http://sunlab.cpy.cuhk.edu.hk/GeneCT/.
Supplementary data are available at Bioinformatics online.
组织活检常用于癌症诊断和分子研究。然而，确定活检的癌症状态和癌症组织的肿瘤起源需要高级技能。正确的分类对于下游实验设计和结果解释至关重要，特别是在分子癌症研究中。因此，迫切需要用于泛癌活检的癌症状态和组织起源的准确分类方法。
我们开发了一种基于深度学习的分类器，命名为 GeneCT，用于预测泛癌活检的癌症状态和组织起源。GeneCT 在来自不同来源的泛癌数据集上表现出优异的性能，优于现有工具。我们相信 GeneCT 有可能促进癌症诊断、肿瘤起源确定和分子癌症研究。
GeneCT 是用 Perl/R 实现的，并支持 GNU/Linux 平台。源代码、测试数据和网络服务器可在 http://sunlab.cpy.cuhk.edu.hk/GeneCT/ 上免费获取。
补充数据可在生物信息学在线获得。
