Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of A Diagnostic Signature Through The Aid of Machine Learning

Schizophrenia (SZ) is a serious psychiatric disease, with a complex genetic basis that affects around 1% of the population worldwide. The symptoms of the disease are divided into positive, negative and cognitive symptoms. Positive symptoms include hallucinations, delusions as well as disorganised speech and behaviour. Negative symptoms include anhedonia, social withdrawal, and lack of motivation and energy. Finally, cognitive symptoms involve cognitive dysfunctions of patients suffering from SZ. Pharmacological treatment of the disease mostly deals with the positive, psychotic symptoms of the disease, but does not improve cognitive and social dysfunction. Moreover, the etiology of SZ predicates upon a combination of genetic and environmental factors, probably in early life, that affect neurogenesis and neuronal plasticity [1]. DNA microarray technologies enabling genome-wide gene expression profiling have been intensely exploited in the last decade, in order to promote the elucidation of the underlying biological mechanisms of SZ [2-5]. These studies, through the high dimensional data that they yield, can prove to be very useful for the generation of diagnostic biomarker signatures in the management of SZ. The usefulness of these data is based on the fact that they may reveal several genes that act synergistically. Probably, the genes that present these synergistic effects with other genes cannot be associated with SZ on their own. The importance of the development of classification models in SZ is great as, at the moment, the diagnosis of the disease is based exclusively on the evaluation of the clinical symptoms after they have manifested. Despite much research effort, some of the most crucial questions regarding SZ have not been answered. The heterogeneity and the multi-factorial background of SZ suggest the study of this disease through statistical methods for the identification of patterns in the data. Differentially expressed genes occurring from microarray experiments can be utilized as classifying biomarkers gain and can reveal underlying genetic factors in relation to important psychiatric diseases, such as SZ [6].