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counterparts [54]. They discovered 61 genes that collectively considerably en-

hance survival prediction.

It is very difficult to diagnose Multiple Sclerosis (MS) early due to its

chaotic and complex nature. Li and coworkers attempted to construct a di-

agnosis model for MS microarray data using peripheral blood RNA [55]. Ac-

cording to their findings, the diagnosis model used in this study had a high

specificity (93.93 %), making it effective for differential diagnosis. In another

study, Loveless and colleagues employed a tissue microarray methodology us-

ing tissue blocks from neocortex and subcortical sites of MS patients (Loveless

et al., 2018). Their research demonstrated complement dysregulation in MS

grey matter lesions, incorporating a relationship between tissue lesions and

the numerical density of C1q+ cells.

Depression is on the rise as a result of genetic susceptibility, growing daily

stress, and global difficulties. Lind and Tsai have compiled microarray studies

regarding major depression disorder (MDD) in hopes of identifying the current

understanding and limits of this disorder [56]. They have also determined the

limitations of these studies. The key drawback in MDD studies was the small

cohort size, which may have resulted in insufficient statistical data for the

identification of important biomarkers. In a more current investigation, Yu

and colleagues used the analysis of microarray data in identifying Arc and

Homer1 involved in both epilepsy and depression [57]. They also identified

mutual pathways such as regulation of angiogenesis and cellular response to

interleukin-1. Feng and coworkers performed bioinformatics analysis on MDD

data from GEO database where they identified several deregulated genes in

connection with the disorder [58].

Although genetics play a significant part in its onset, the exact cause of

schizophrenia (SCZ) is still unknown. Therefore, determining the origins of

SCZ is essential to enhancing the effectiveness of treatments and the prog-

nosis of those who suffer from the condition. Wagh and associates performed

a systematic assessment of peripheral blood microarray studies using SCZ

patients’ and healthy controls’ blood. They investigated 61 studies on gene

expression, of which 17 used microarrays and two used RNA sequencing [59].

Microarray study outcomes compared between drug-naive and drug-treated

SCZ patients revealed discrepancies. They concluded that cohort studies in-

cluding a variety of groups, the application of high-throughput sequencing

technologies, and the use of computational analysis based on artificial in-

telligence (AI) will considerably advance our comprehension and diagnostic

capacities for this complicated condition. Long non-coding RNAs (lncRNAs)

were the subject of an investigation by Wang et al. using a microarray dataset

to examine how they altered the molecular mechanisms and pathways under-

lying SCZ pathophysiology [60]. Their findings suggested the pathophysiology

of the disease involved a competing endogenous RNAs subnetwork that may

be employed as possible diagnostic biomarkers. In another study, three mi-

croarray datasets were used in a meta-analysis carried out by Piras et al. to