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Detecting cancers at early stages can dramatically reduce mortality rates.Therefore, practical cancer screening at the population level is needed. Here,we develop a comprehensive detection system to classify all common cancertypes. By integrating artificial intelligence deep learning neural network andnoncoding RNA biomarkers selected from massive data, our system can accuratelydetect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of aReceiver Operating Characteristic curve). Intriguinely, with no more than 6biomarkers, our approach can easily discriminate any individual cancer type vsnormal with 99% to 100% AUC. Furthermore, a comprehensive marker panel cansimultaneously multi-classify all common cancers with a stable 78% of accuracyat heterological cancerous tissues and conditions. This provides a valuableframework for large scale cancer screening. The AI models and plots of resultswere available in https://combai.org/ai/cancerdetection/

Noncoding RNAs and deep learning neural network discriminate multi-cancer types