PMC:1624833 / 30335-31392 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1624833","sourcedb":"PMC","sourceid":"1624833","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1624833","text":"5 Conclusion\nThe biomedical community has access to large quantities of publicly-available gene expression datasets. Biclustering has emerged as a powerful methodology for analyzing these datasets. In this paper, we have introduced a novel algorithm for laying out biclusters in a two-dimensional matrix so as to reveal the overlaps and relationships between the biclusters. The algorithm performs efficiently in practice. We have demonstrated the applicability of the algorithm to three important problems in bioinformatics using both binary and real-valued data. An easy-to-use web interface distributed with the layout software allows the user to query and navigate layouts that are too large to study manually. Biclustering is useful not just for processing gene expression data but for any dataset that measures the relationships between two different types of data, e.g., genes and functions; microRNAs and their target mRNAs; and genes and diseases. Thus, our algorithm has the potential to be useful for a wide variety of bioinformatic applications.","divisions":[{"label":"title","span":{"begin":0,"end":12}}],"tracks":[]}