Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
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DOI: 10.1038/s41467-020-17347-6
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References listed on IDEAS
- Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
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- Bangfeng Wang & Yiwei Li & Mengfan Zhou & Yulong Han & Mingyu Zhang & Zhaolong Gao & Zetai Liu & Peng Chen & Wei Du & Xingcai Zhang & Xiaojun Feng & Bi-Feng Liu, 2023. "Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
- Xiaohua Xing & Linsheng Cai & Jiahe Ouyang & Fei Wang & Zongman Li & Mingxin Liu & Yingchao Wang & Yang Zhou & En Hu & Changli Huang & Liming Wu & Jingfeng Liu & Xiaolong Liu, 2023. "Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
- Yao Yao & Xueping Wang & Jian Guan & Chuanbo Xie & Hui Zhang & Jing Yang & Yao Luo & Lili Chen & Mingyue Zhao & Bitao Huo & Tiantian Yu & Wenhua Lu & Qiao Liu & Hongli Du & Yuying Liu & Peng Huang & T, 2023. "Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
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