Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

• Published in: Nature communications Vol. 15; no. 1; pp. 1657 - 13
• Main Authors: Chen, Yangzi, Wang, Bohong, Zhao, Yizi, Shao, Xinxin, Wang, Mingshuo, Ma, Fuhai, Yang, Laishou, Nie, Meng, Jin, Peng, Yao, Ke, Song, Haibin, Lou, Shenghan, Wang, Hang, Yang, Tianshu, Tian, Yantao, Han, Peng, Hu, Zeping
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 101/58; 140/58; 631/114/1305; 631/67/1504/1829; 631/67/1857; 631/67/2322; 631/67/2327; Biomarkers; Cancer; Diagnosis; Endoscopy; Gastric cancer; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Medical prognosis; Metabolic Reprogramming; Metabolism; Metabolites; Metabolomics; multidisciplinary; Precision Medicine; Prognosis; Risk groups; Science; Science (multidisciplinary); Sensitivity; Sensitivity analysis; Stomach Neoplasms - diagnosis

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient ris...