AGRA: Analysis of gene ranking algorithms

Simon Kocbek, Rune Sætre, Gregor Stiglic, Jin Dong Kim, Igor Pernek, Yoshimasa Tsuruoka, Peter Kokol, Sophia Ananiadou, Jun'ichi Tsujii

    Research output: Contribution to journalArticlepeer-review


    Summary: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. © The Author(s) 2011. Published by Oxford University Press.
    Original languageEnglish
    Article numberbtr097
    Pages (from-to)1185-1186
    Number of pages1
    Issue number8
    Publication statusPublished - Apr 2011


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