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    <p>I would consider two approaches:</p> <ol> <li><p>As you are suggesting, run your false set (or rather, multiple permutations, i.e. multiple false sets) as your additional test sets in the SVM and compare the scores with the real test set. Essentially, you would want to show that your real test set performs significantly better than most of your false sets. This would be in the spirit of a statistical test described, for example, in <a href="http://www.biomedcentral.com/1471-2105/13/210" rel="nofollow noreferrer">this paper</a> for more complex data. Also, <a href="http://cseweb.ucsd.edu/users/elkan/254spring01/jdrishrep.pdf" rel="nofollow noreferrer">this paper</a> may be useful for converting SVM scores into calibrated probabilities using a binning approach. </p></li> <li><p>Build a two-class SVM using a subset of the <code>false</code> set as the second training set. The classification task will then be to ascertain to which class your gene expression pattern is more likely to belong: the "positive" class or the "false" one. <a href="http://hal.archives-ouvertes.fr/docs/00/10/39/55/PDF/cr102875872670.pdf" rel="nofollow noreferrer">This paper</a>, <a href="https://stackoverflow.com/questions/6395986/classification-score-svm">this thread</a> and <a href="https://stats.stackexchange.com/questions/12068/classification-score-svm">this thread</a>, as well as general SVM textbooks, may be helpful in deciding on how best to design this two-class classifier.</p></li> </ol> <p>Hope it helps. </p>
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    1. COthank you, i will try to get the svm running and may open another thread. I'll check the paper and threads.
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