Multiobjective Simultaneous Gene Ranking and Clustering

Microarray technology allows us to examine how genes express themselves in different biological experiments simultaneously. The analysis of gene expression data involves creating a matrix from raw microarray data and employing clustering algorithms, such as hierarchical and partitional, to discern patterns. However, the existing methods lack a nuanced approach to highlight the specific importance of individual genes. This gap in methodology serves as the motivation behind the development of MOSCFRA (Multiobjective Simultaneous Clustering and Feature Ranking), a simultaneous clustering and gene ranking algorithm presented in this chapter. Unlike conventional techniques, MOSCFRA acts as a discerning investigator for genes. It not only groups genes together based on shared expression patterns but also assigns ranks to each gene, providing insights into their relative significance. By employing a sophisticated multiobjective optimization framework, MOSCFRA enhances our ability to uncover hidden patterns and connections within gene behavior. This innovative approach contributes to the broader goals of disease diagnosis and drug development, offering practical applications that can revolutionize the field.