چکیده مقاله
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
کلیدواژهها
نویسندگان
Mohadese Maktabi
Department of Biological Science and Technology , kherad institute , Persian Gulf University, Bushehr, Iran
Moslem Momen
Computer Engineering Department , Islamic Azad University Bandar Abbas Branch, Hormozgan, Iran
شیوه ارجاع
Maktabi, Mohadese and Momen, Moslem,1403,Deciphering the TSH-Associated Gene Network: A Comparative Analysis Using Machine Learning Clustering Algorithms,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر