Publication

Abstract : Even though biological data analysis helps in understanding the chemical processes, handling them is difficult due to their abundant size, heterogeneous nature and access time overheads. Complex disorder diagnostics can be carried out effectively by recognizing the most conformant genes from a set of candidate genes which are having a higher association with the disorder. Traditional gene analysis methods such as gene mutation analysis, single nucleotide polymorphism (SNP) detection and other wet lab techniques are delimited by several factors such as high-cost clinical experiments, unpredictable time consumption and insufficient prior knowledge about genetic materials. They are replaced by efficient computational solutions due to extensive advantages like economical computational cost, appropriate testing and validation strategies, adequate prior information etc. This paper contains a thorough literature review about prevailing methods, tools and data sources primarily used for computational gene prioritization. The aggregation, analysis, interpretation and comparison of different gene prioritization strategies are done with a view to provide an insight into recent trends and traits persist in them. Different validation methods commonly used for gene prioritization are also analysed in this study.