How can multiobjective optimization help with gene data?
Analyzing gene expression data often involves multiple, competing goals. For example, when clustering genes, we might want to group genes with similar expression patterns (a quality of clustering objective) while also ensuring the number of clusters is biologically meaningful or interpretable (another objective). Multiobjective optimization techniques allow us to explore solutions that balance these different criteria, providing a set of Pareto-optimal clusterings. This helps researchers understand the biological relationships between genes by considering diverse criteria simultaneously, rather than optimizing for a single aspect in isolation.
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