Motivation

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.

Results

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.

Availability and implementation

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 information

Supplementary data are available at Bioinformatics online.

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