Investigation on the effect of Genetic mutants in neurodegenerative Brain disorders A computational study

Abstract

Biological data mining aims at employing machine learning methods to analyze and evaluate biological and clinical data. Machine learning is the new version of the data mining process employed by researchers to identify potential biomarkers for diagnosing the disease. Neurogenomics is a relatively new emerging area in research that intends to use high throughput machine learning techniques to identify gene mutations associated with brain disorders. This led to the development of Computational Neurogenomics that aims at utilization of computational methods to gain a better understanding of the nervous system as a whole from a genome perspective. newlineThis research specifically targeted the use of computational methods to explore the effect of genetic mutations (mutants) and their role in causing potential brain disorders like Alzheimer s or Parkinson s disease. This requires an in-depth analysis of the gene and protein sequences, their physico-chemical properties that contribute to the onset of the disease. This work will also aim at determining specific gene and protein features that could differentiate between Alzheimer s and Parkinson s disease that will eventually aid in design of appropriate drugs. The ultimate target is to predict optimal number of biomarkers for brain disorders with improved accuracy and faster processing by analysis of gene and protein data through computational methods. newlineThe previous research which involved the use of data mining methods in detecting the oncogenomic protein patterns from structural and physico-chemical properties of protein sequences inspired the current research to apply the same idea on discovering protein patterns on brain disorders newline