In Brassicaceae, a self-incompatibility (SI) system mediates pollen-pistil interactions. Self-pollen could be recognized and rejected by incompatible pistils. Several components involved in the SI response have been determined, including S-locus receptor kinase (SRK), S-locus cysteine-rich protein/S-locus protein 11, and arm repeat-containing protein 1 (ARC1). However, the components involved in the SI system of Brassicaceae are not fully understood.
Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data.