O uso de redes neurais artificiais na análise de dados de câncer de pulmão
Lung cancer represents the leading cause of cancer death worldwide and has a high incidence. Like other types of cancer, it can occur due to different causes, from genetics to environmental ones, so studies carried out using different types of data may be relevant for the control of this neoplasm...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/48470 |
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| Resumo: | Lung cancer represents the leading cause of cancer death worldwide and has a
high incidence. Like other types of cancer, it can occur due to different causes, from
genetics to environmental ones, so studies carried out using different types of data
may be relevant for the control of this neoplasm, especially when considering
factors that have an impact on patient survival. In the context of lung cancer, this
study uses deep learning to predict patient survival. Clinical and molecular data
from TCGA (The Cancer Genome Atlas) databases were obtained for the LUSC
(Lung Squamous Cell Carcinoma) and LUAD (Lung Adenocarcinoma) cohorts,
followed by the analysis of the genomic alterations, and application of neural
networks using as input the frequently mutated genes for each cohort, selection of
key genes and validation with another database. The cohorts showed differences in
survival among themselves when subjected to the Kaplan-Meier method and the
Log-Rank test. In the genomic analysis, all genes with a mutation frequency above
15% were selected, and 34 genes were found for LUAD and 32 for LUSC. The use
of these genes as input in the constructed networks made it possible to generate
the LUSC and LUAD networks with 100% accuracy, identifying, according to the
mutations, the vital status of the patient. In addition, a LUSC network was also
obtained using another LUSC-KR database as validation, which reached 99%
accuracy. In this way, this work showed that the use of genes with frequent
mutations associated with deep learning is a robust tool and allows predicting the
survival of patients with lung cancer. |
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