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

by   Anyou Wang, et al.

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 Receiver Operating Characteristic curve). Intriguinely, with no more than 6 biomarkers, our approach can easily discriminate any individual cancer type vs normal with 99 simultaneously multi-classify all common cancers with a stable 78 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


page 1

page 2

page 3

page 4


A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Oral cancer incidence is rapidly increasing worldwide. The most importan...

Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network

Gastric cancer is one of the most common cancers, which ranks third amon...

Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations

Colorectal cancer (CRC) is one of the most common types of cancer with a...

Advances in Artificial Intelligence to Reduce Polyp Miss Rates during Colonoscopy

BACKGROUND AND CONTEXT: Artificial intelligence has the potential to aid...

Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Background: An increasing volume of prostate biopsies and a world-wide s...

Please sign up or login with your details

Forgot password? Click here to reset