Construction Of Gene Pattern Recognition Algorithm And Integration Platform Based On Canceromics Data

With the rapid development of human genome sequencing technology,many scientific research institutions are carrying out some large-scale sequencing projects,some famous international genomics projects,such as the Cancer and Tumor Gene Atlas（TCGA）project,these projects provide researchers with a large amount of genomics data to facilitate the exploration of the pathogenic mechanism of different cancer types.Numerous biological data make it possible for researchers to understand the heterogeneity of different types of cancer.More and more studies have proved that the occurrence and formation of cancer are caused by somatic mutations,and distinguishing driver mutations and concomitant mutations in somatic mutations is the key to understanding cancer progression.Based on the somatic mutation data of cancer patients,many methods for identifying cancer-driven pathways have been developed.The lack of user-friendly integrated tools limits their applications.The analysis of single-cell spatial transcriptome data also has considerable value in the field of complex disease research.It can not only capture rare cell types and analyze the complex structure of tumors,but also provide further insights for studying the composition of tumor microenvironment.Research based on canceromics data helps to understand the occurrence and development of tumors more deeply,discover cancer-related diagnostic targets and related treatment targets,and contribute to the development of cancer precision medicine.Improving data quality is one of the effective ways to improve the accuracy of subsequent analysis results.Due to the impact of sequencing technology,a certain amount of noise is brought to the sequencing results.Therefore,only using the original sequencing data for analysis may affect the research results.Aiming at the problem of missing single-cell spatial transcriptome data,this paper proposes a pattern recognition model to fill in missing values in the data that may be caused by technical noise.Through the real tumor slice data,the validity of the model is confirmed.In this paper,an online web application integration platform is developed for eight typical cancer-driven pathway identification methods.The platform integrates algorithms written in different programming languages into the same software project through hybrid programming technology,providing a unified operation mode and visualization Represents and standardizes the input and output formats of data.The feasibility and efficiency of the integrated platform was verified through the application of 14 kinds of real cancer data,and some meaningful explorations were made on the results.Experimental results show that although gastric adenocarcinoma and esophageal adenocarcinoma in esophageal gastric cancer have related driver gene sets,no common gene set has been detected for these two subtypes.This indicates that the two subtypes of gastric adenocarcinoma and esophageal adenocarcinoma may have different molecular mechanisms in the occurrence and development of cancer.The integrated platform may become a powerful tool for cancer-driven pathway research.