Immune/neural approach to characterize salivary gland neoplasms (SGN)
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Universidade Federal de Minas Gerais
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Artigo de periódico
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The purpose of the current study was to use immune-inspired algorithm ClonALG whose performance is increased by using the Kohonen neural network training algorithm (Immune/Neural approach), to characterize the nature of salivary gland neoplasms (SGNs). The leader gene approach in order to identify biomarkers for SGNs. Extensive data were obtained for each of the 35 types of neoplasms. The gene leaders for each type of SGN were identified in a table and then divided according to the two different methods: K-means clustering and Immune/Neural approach. Genes related to SGNs were identified using PubMed, OMIM and Genecards databases. A bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each nature of an SGNs. The weighted number of links (WNL) and total interactions score (TIS) values were then obtained. Finally, the genes were clustered, and the gene leaders were identified using the K-means clustering method and the Immune/Neural approach.
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Tumores, Glândulas salivares, Bioinformática
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https://www.sciencedirect.com/science/article/pii/S1568494619306581