Research Information System
J. Amasya Univ. Inst. Sci. Technol., vol.5, no.1, pp.100-104, 2024 (Peer-Reviewed Journal)
Abstract − Vortioxetine is a pharmacological agent that acts as a serotonin modulator and stimulant, with safety and tolerability being important health issues. The goal of this study was to use bioinformatic and machine learning methods to find differentially expressed genes (DEG) between rats exposed to vortioxetine and matched controls. The GSE236207 dataset (Rattus norvegicus) was obtained from the NCBI and analyzed with R, followed by GO and KEGG enrichment analyses, and String's protein-protein interaction network was established to identify important genes. In the second step, the original datasets were preprocessed by detecting and correcting missing and noisy data, and then merged. After feature selection for the cleaned dataset, machine learning algorithms such as K-nearest neighbors’ algorithm, Naive Bayes, and Support Vector Machine were used. In addition, an accuracy of 0.90 was observed with Support Vector Machine. Leveraging these techniques, the study linked IGFBP7, KLRA22, PROB1, SHQ1, NTNG1, and LOC102546359 to vortioxetine exposure. The bioinformatic analysis revealed 18 upregulated genes and 27 downregulated genes, with all approaches identifying only one common locus, LOC102546359, responsible for ncRNA synthesis. The crucial point is that this locus bears no connection to any disease or trigger mechanism, thereby bolstering the safety of vortioxetine.