Predicting drug responsiveness by citalopram induced pathway regulations and biomarker discovery in lymphoblastoid cell lines from depression affected individuals

Unipolar Disorder, more commonly identified as Major Depressive Disorder is a psychiatric condition characterized by lack of interest, constant mood swings, and persistent feelings of sadness. Individuals affected by this disorder exhibit poor interest in external stimuli. Clinical depression is a severe form, needs medical support to regulate and restore the patients through proper medications. Early diagnosis of depression cases can be treated with effective procedures, the recovery is rapid. Traditional methods equipped to diagnose mental illness sometimes mislead the medical practitioners while identifying the exact condition. This happens due to vague symptoms observed from psychiatric individuals of different categories such as bipolar disorder, schizophrenia, depression, and post-traumatic stress disorder. It eventually leads to misdiagnosis, followed by medication, ending up with erroneous treatment. Genetic studies often proved to be successful with accurate disease diagnosis, shows evidence with the trace of the disease pathway alongside a relationship with other factors. In recent times, many studies conducted on mental health under the genome level, say drug therapy responses and adverse effects. In this study, the gene expressions of the individuals affected by depression are analyzed after being medicated with citalopram, an antidepressant used to treat psychiatric patients. The biomarkers are identified by applying state-of-the-art feature selection methods. The regulations of genes are traced in this experiment from the biomarkers identified. The performance of the identified biomarkers is evaluated with supervised machine learning classification algorithms. The mathematical models reveal different insights from the subset. Accuracy, precision, and f-score are calculated for the models benchmarked in this study. The outcome of the study exhibits the significance of the proposed pipeline and was found to be efficacious for analyzing the medications and their target responses in practice.