{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:53:54Z","timestamp":1781308434643,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six 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 common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with &gt;96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with &gt;82% accuracy.<\/jats:p>","DOI":"10.3390\/cancers14020352","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T04:15:25Z","timestamp":1641960925000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-3606","authenticated-orcid":false,"given":"Anyou","family":"Wang","sequence":"first","affiliation":[{"name":"The Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA 92521, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Hai","sequence":"additional","affiliation":[{"name":"The Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA 92521, USA"},{"name":"Department of Microbiology and Plant Pathology, University of California at Riverside, Riverside, CA 92521, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"Rider","sequence":"additional","affiliation":[{"name":"Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Skip Bertman Drive, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianchuan","family":"He","sequence":"additional","affiliation":[{"name":"Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1038\/d41586-020-00847-2","article-title":"How AI is improving cancer diagnostics","volume":"579","author":"Savage","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1038\/d41586-020-00845-4","article-title":"How cancer genomics is transforming diagnosis and treatment","volume":"579","author":"Nogrady","year":"2020","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1038\/s41586-019-1272-6","article-title":"Genome-wide cell-free DNA fragmentation in patients with cancer","volume":"570","author":"Cristiano","year":"2019","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1038\/s41568-020-0241-6","article-title":"Tumour heterogeneity and the evolutionary trade-offs of cancer","volume":"20","author":"Hausser","year":"2020","journal-title":"Nat. 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