Method for predicting liver cancer risk through high-throughput analysis of hepatitis B virus genome RT/S region sequence characteristics by machine learning model

Abstract

The invention relates to a method for predicting liver cancer risk through high-throughput analysis of hepatitis B virus genome RT / S region sequence characteristics by a machine learning model, and belongs to the technical field of genome sequence calculation. The method comprises the steps of: selecting a corresponding feature calculation method to complete construction of an RT / S region sequencefeature matrix of the HBV genome; and training the data set by adopting a machine learning model, and finally predicting the risk probability of suffering from liver cancer. The method based on artificial intelligence is characterized in that a series of feature algorithms are applied to learning of machine learning model training in the field of HBV genome RT / S region sequence data processing; high-dimensional genome data sequencing signals are converted into clinical risk degree judgment of suffering from diseases, and computer-assisted high-risk early warning and early diagnosis of suffering from liver cancer of HBV infected people are facilitated.

Description

technical field [0001] The invention relates to a method for predicting the risk of liver cancer by using a machine learning model for high-throughput analysis of the RT / S region sequence characteristics of the Hepatitis B virus (HBV) genome, belonging to the technical field of genome sequence calculation, and in particular to the HBV genome RT / S The method of calculation of regional sequence features and machine learning model training and prediction is used for high-risk early warning and early diagnosis of liver cancer in HBV-infected populations, and has clinical application value. Background technique [0002] Hepatocellular carcinoma (HCC) is the most common primary liver cancer, the third leading cause of cancer death, and the sixth most common cancer in the world. HBV infection is one of the most important risk factors. According to the estimates of the World Health Organization (WHO), about 257 million people in the world have been confirmed as positive for hepatit...

AI Technical Summary

Problems solved by technology

However, so far, few studies have analyzed and studied the characteristics of HBV RT gene mutations in patients with CHB and HBV-related HCC by using big data generated by NGS technology.

Furthermore, traditional statistical algorithms appear insufficient to handle such high-dimensional genomic data to convert quality sequencing signals into actionable biomarkers that can be used in the clinic

Images