Lung Cancer Stage Prediction Using Multi-Omics Data

Autor:	Wei Li, Binchun Liu, Weiqian Wang, Can Sun, Jianpeng Che, Xuelian Yuan, Chunbo Zhai
Rok vydání:	2022
Předmět:	Lung Neoplasms Article Subject General Immunology and Microbiology Gene Expression Profiling Applied Mathematics Modeling and Simulation Humans General Medicine Transcriptome Algorithms General Biochemistry Genetics and Molecular Biology Neoplasm Staging
Zdroj:	Computational and Mathematical Methods in Medicine. 2022:1-10
ISSN:	1748-6718 1748-670X
Popis:	Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f10e9e09e230498658baba3cd12726c https://doi.org/10.1155/2022/2279044 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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