Identification of gene profiles related to the development of oral cancer using a deep learning technique

Autor: Leili Tapak, Mohammad Kazem Ghasemi, Saeid Afshar, Hossein Mahjub, Alireza Soltanian, Hassan Khotanlou
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: BMC Medical Genomics, Vol 16, Iss 1, Pp 1-12 (2023)
Druh dokumentu: article
ISSN: 1755-8794
DOI: 10.1186/s12920-023-01462-6
Popis: Abstract Background Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. Methods Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P
Databáze: Directory of Open Access Journals
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