Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noncoding RNAs and deep learning neural network discriminate
  multi-cancer types   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 cancer
types. By integrating artificial intelligence deep learning neural network and
noncoding RNA biomarkers selected from massive data, our system can accurately
detect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of a
Receiver Operating Characteristic curve). Intriguinely, with no more than 6
biomarkers, our approach can easily discriminate any individual cancer type vs
normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can
simultaneously multi-classify all common cancers with a stable 78% of accuracy
at heterological cancerous tissues and conditions. This provides a valuable
framework for large scale cancer screening. The AI models and plots of results
were available in https://combai.org/ai/cancerdetection/

18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
2
The categories of this paper are: math