Review of Gene Selection and Prediction Using Data Mining Methods
Whilst the genomes of several organisms have been sequentially ordered during the past few years, transformation of such crude sequence data into information is a difficult task. The recognition of genes or genomics in traditional human and plant diseases frequently needs time‐consuming and costly investigation of a huge number of probable candidate genes, as genome‐wide methods like linkage analysis and relationship studies often choose several hundreds of ‘positional’ candidates. In the current work carried out, a variety of prediction techniques have been designed, which attempt to solve one segment of this problem that comprises of finding the genes or genome. This research work provides the review on the available approaches for choosing the genes or genomics and predicting what forms the basis of their inherent benefits and constraints. This work of review also discusses about the details behind an elementary gene selection and classification mechanisms for disease‐gene prediction and then it is applied for exhaustive review of bioinformatics tools, which have been evolved for this, with focus on the ideological aspects than the technical information and performance. The important gene selection techniques and computational algorithms followed are also explained briefly. Here, in this review work, a generic overview on the traditional gene finding techniques are studied first, without delving too deep into the mathematical techniques and algorithms. At last, few of the past successes acquired by classification scheme for illustrating their advantageous contribution made to medical research is also discussed.