deciphering the tsh-associated gene network: a comparative analysis using machine learning clustering algorithms

In this study, we conducted gene ontology (go) and pathway analyses to identify the go terms most closely related to thyroid-stimulating hormone (tsh) and to infer the protein-protein interaction network using three machine learning clustering algorithms: k-means, mcl (markov clustering algorithm), and dbscan (density-based spatial clustering of applications with noise). we analysed a collection of 112 tsh-associated genes reported in the literature to date. our analysis identified 12 go terms for molecular function (mf), 259 terms for biological process (bp), and 3 terms for cellular component (cc), along with 17 kegg, 16 reactome, and 11 wiki pathways in the pathway analysis. of these, 5 mf, 10 bp, and 2 cc go terms were significant, however, no pathways were detected as significant at the p-value=0.05 level. the clustering algorithms yielded similar results, notably highlighted akt1, tshr, gnas, gata3, and kdr as key hub genes in the network.