BARTMAP: A viable structure for biclustering

Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and drug testing, and genetic regulatory network inference. However, such a practice is inherently limited due to the existence of many uncorrelated genes with respect to sample or condition clustering, or many unrelated samples or conditions with respect to gene clustering. Biclustering offers a solution to such problems by performing simultaneous clustering on both dimensions, or automatically integrating feature selection to clustering without any prior information, so that the relations of clusters of genes (generally, features) and clusters of samples or conditions (data objects) are established. However, the NP-complete computational complexity raises a great challenge to computational methods for identifying such local relations. Here, we propose and demonstrate that a neural-based classifier, ARTMAP, can be modified to perform biclustering in an efficient way, leading to a biclustering algorithm called Biclustering ARTMAP (BARTMAP). Experimental results on multiple human cancer data sets show that BARTMAP can achieve clustering structures with higher qualities than those achieved with other commonly used biclustering or clustering algorithms, and with fast run times.