Comparative Study of Microarray Based Disease Prediction - A Survey

Abstract

Recognition of genetic expression becomes an important issue for research while diagnosing genetic diseases. Microarrays are considered as the representation for identifying gene behaviors that may help in detection process. Hence, it is used in analyzing samples that may be normal or affected, also in diagnosing various gene-based diseases. Various clustering and classification techniques were used to face the challenges in handling microarray. High dimensional data is one of the major issues caused while handling microarray. Also because of this issue, possibilities of redundant, irrelevant and noisy data may occur. To solve this problem feature selection process which optimally extracts the features is introduced in clustering in classification techniques. This survey observes some various techniques of classification, clustering of genes and feature selection methods such as supervised, unsupervised and semi-supervised methods. To determine the suitable semi-supervised algorithm that combines and analyze for detecting new or difficult mutated disease. This survey shows that how semi-supervised approach evolves and outperforms the existing algorithms.