Cargando…
Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high perfo...
Autores principales: | Huang, Lin, Wang, Lin, Hu, Xiaomeng, Chen, Sen, Tao, Yunwen, Su, Haiyang, Yang, Jing, Xu, Wei, Vedarethinam, Vadanasundari, Wu, Shu, Liu, Bin, Wan, Xinze, Lou, Jiatao, Wang, Qian, Qian, Kun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366718/ https://www.ncbi.nlm.nih.gov/pubmed/32678093 http://dx.doi.org/10.1038/s41467-020-17347-6 |
Ejemplares similares
-
Metabolic Fingerprinting on a Plasmonic Gold Chip for Mass Spectrometry
Based in Vitro Diagnostics
por: Sun, Xuming, et al.
Publicado: (2018) -
Plasmonic silver nanoshells for drug and metabolite detection
por: Huang, Lin, et al.
Publicado: (2017) -
Integrative Serum Metabolic Fingerprints Based Multi‐Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification
por: Wang, Lin, et al.
Publicado: (2022) -
Single-Cell Spatial MIST for Versatile, Scalable Detection of Protein Markers
por: Meah, Arafat, et al.
Publicado: (2023) -
Identification of A Panel of Serum microRNAs as Biomarkers for Early Detection of Lung Adenocarcinoma
por: Lv, Shaogang, et al.
Publicado: (2017)