Research & Publication
Abstract:Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. Interpreting gene expression data remains a difficult problem and an active research area due to their native nature of high dimensional low sample size. These issues poses great challenges to existing classification methods. Thus effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to improve treatment strategies. Small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. Particular, the study report focus on the most used data mining techniques for gene selection and semi supervised cancer classification. In addition, it provides a general idea for future improvement in this field.