Wuhan University

Fubing Wang

Fei Long

Wei Zhong

Changzhong Jiang

Xiangheng Xiao

Shilian Dong

A spectroscopy and artificial intelligence-interaction serum analysis method includes: collecting bulk SERS spectral data of clinical serum samples, performing dimension reduction on the spectral data by using a covariance matrix to obtain spectral different peak positions of cancer patients and normal individuals, and performing spectral data processing and algorithm identification by using an svm model of an artificial intelligence algorithm to obtain a cancer identification rate. Compared with the conventional serum analysis method, the spectroscopy and artificial intelligence-interaction serum analysis method requires no antibody-antigen or other biological specificity modification processes, and the serum of cancer patients and normal individuals can be identified more cheaply, rapidly and accurately. Also the different peak positions in SERS spectra of a large amount of serum samples can be located, which provides an entirely novel detection and analysis method at a molecular bond energy level for the field of liquid biopsy of clinical cancers.

Interaction of spectroscopy and artificial intelligence for serological analysis and its applications

The disclosure belongs to the field of genetic testing and biomedicine, relating to a method for constructing a prognostic model of hepatoma and an application thereof, comprising 1) obtaining and identifying fibroblasts with high FAP expression; 2) obtaining and identifying TAMs; 3) analyzing co-localization between fibroblasts with high FAP expression obtained and the TAMs obtained previously; 4) communicating and analyzing the fibroblasts with high FAP expression after the localization in the Step 3) with TAMs to obtain CCC ligand-receptor genes; 5) screening the CCC ligand-receptor genes obtained previously based on machine learning to obtain key CCC ligand-receptor genes; and 6) constructing a prognostic model of hepatoma according to the key CCC ligand-receptor genes obtained in the Step 5). The present disclosure provides a method for constructing a prognostic model of hepatoma that can be applied to auxiliary judgment of the prognosis of hepatoma patients and an application thereof.

Growing community of inventors

Wuhan, China

Fubing Wang