Classification of microarray gene expression cancer data by using artificial intelligence methods

Today, the development of computer technologies has affected the studies in many areas. Advances in molecular biology and computer technologies have revealed the science of bioinformatics. Rapid developments in the field of bioinformatics have contributed greatly to the solution of many problems waiting to be solved in this field. The classification of DNA microarray gene expressions is one of these problems. DNA microarray studies are a technology used in the field of bioinformatics. DNA microarray data analysis plays a very effective role in the diagnosis of diseases related to genes such as cancer. By determining gene expressions depending on the type of disease, it can be determined with great success rate whether any individual possesses the diseased gene. The use of high-performance classification techniques on microarray gene expressions is of great importance to determine whether an individual is healthy. There are many methods for classifying DNA microarrays. Support Vector Machines, Naive Bayes, k-Nearest Neighbour, Decision Trees, such as many statistical methods are widely used. However, when these methods are used alone, they do not always give high success rates in classifying microarray data. Therefore, the use of artificial intelligence-based methods to achieve high success rates in the classification of microarray data is seen in the studies. In this study, in addition to these statistical methods, it is aimed to obtain higher success rates by using a method such as ANFIS based on artificial intelligence. K-Nearest Neighbourhood, Naive Bayes and Support Vector Machines were used as statistical classification methods. Here, studies on two different cancer data, namely breast and central nervous system cancer, have been conducted. According to the information obtained from the results, it was found that artificial intelligence based ANFIS technique was more successful than statistical methods.