Gene expression studies with DGL global optimization for the molecular classification of cancer

Abstract

This paper combines a powerful algorithm, called Dongguang Li (DGL) global optimization, with the methods of cancer diagnosis through gene selection and microarray analysis. A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is proposed and applied to two test cancer cases, colon and leukemia. The study attempts to analyze multiple sets of genes simultaneously, for an overall global solution to the gene's joint discriminative ability in assigning tumors to known classes. With the workable concepts and methodologies described here an accurate classification of the type and seriousness of cancer can be made. Using the orthogonal arrays for sampling and a search space reduction process, a computer program has been written that can operate on a personal laptop computer. Both the colon cancer and the leukemia microarray data can be classified 100% correctly without previous knowledge of their classes. The classification processes are automated after the gene expression data being inputted. Instead of examining a single gene at a time, the DGL method can find the global optimum solutions and construct a multi-subsets pyramidal hierarchy class predictor containing up to 23 gene subsets based on a given microarray gene expression data collection within a period of several hours. An automatically derived class predictor makes the reliable cancer classification and accurate tumor diagnosis in clinical practice possible.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

“…This link can be explained by research involving the microarray technology, which makes it possible to detect deletions and duplications in the human genome by analyzing the expression of thousands of genes in different tissues. It is also possible to confirm the importance of genetic screening not only for cancer, but for several diseases, such as ‘ALZHEIMER’ and other brain disorders, thereby assisting in preventive medicine and enabling more efficient treatment plans [ 79 ]. For example, a research was carried out to analyze complex brain disorders such as schizophrenia from expression gene microarrays [ 80 ].…”

SSiCP: a new SVM based Recursive Feature Elimination Algorithm for Multiclass Cancer Classification

“…dimension of the feature space, the excessive noise, and the relatively small sample sizes in DNA microarray data, this issue is under active research in the data mining of gene expression profiles. Especially for data with a large number of cancer types, many conventional classification methods show very poor performance [9], such as the NCI60 data set (9 types of cancer) [5], and the GCM data set (14 types of cancer) [6].Many researchers proposed some other new methods [10][11][12][13][14][15][16]. However, obtaining higher classification accuracy for choosing fewer genes is possible by using more powerful data mining algorithms.…”

“…Many researchers proposed some other new methods [10][11][12][13][14][15][16]. However, obtaining higher classification accuracy for choosing fewer genes is possible by using more powerful data mining algorithms.…”