An ANN model for the differential diagnosis of tuberculosis and sarcoidosis.

Autor:	Vijayaraj M; Department of Bioinformatics, Faculty of Biomedical Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed To Be University)., Abhinand PA; Department of Bioinformatics, Faculty of Biomedical Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed To Be University)., Venkatesan P; Department of Bioinformatics, Faculty of Biomedical Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed To Be University)., Ragunath PK; Department of Bioinformatics, Faculty of Biomedical Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed To Be University).
Jazyk:	angličtina
Zdroj:	Bioinformation [Bioinformation] 2020 Jul 31; Vol. 16 (7), pp. 539-546. Date of Electronic Publication: 2020 Jul 31 (Print Publication: 2020).
DOI:	10.6026/97320630016539
Abstrakt:	Sarcoidosis is often misdiagnosed as tuberculosis and consequently mistreated owing to inherent limitations in histopathological and radiological presentations. It is known that the differential diagnosis of Tuberculosis and Sarcoidosis is often non-trivial and requires expertise and experience from clinicians. Therefore, it is of interest to describe a multilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptional microarray. Genes that are significantly upregulated in Pulmonary Tuberculosis and Sarcoidosis in comparison with healthy controls were used in the model. The model classified Pulmonary Tuberculosis and Sarcoidosis with 95.8% accuracy. The model also helps to identify gene markers that are differentially upregulated in the two clinical conditions. (© 2020 Biomedical Informatics.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu