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False positive membership indicates that a state or agent identifier is hashed in a bloom filter when it is actually not hashed.
An object, therefore, can have positive membership degrees for more than one cluster, revealing the degree of concordance between features of the object and the cluster.
The first term of Eq. (6) is the sum of products of the weight and negative membership of positive training data, and the second term is the sum of products of the weight and positive membership of negative training data.
HG-enrichment analysis applies a binary 'included-or-not included' criterion to assess the positive membership of the genes from a gene set in a selected spot-cluster.
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Bloom filters provide tremendous space savings at a cost of false positive memberships.
Transcriptional regulators that have high positive module membership in the black module but lack binding by the pluripotency TFs are of interest since their strong module membership cannot be explained via regulation by these TFs.
Finally, if more than one positive mutual membership is detected (o>1 in lines 16-18), multiple false positives occur that cannot be identified.
If there is one positive mutual membership detected (o=1 in lines 10-15), the system can derive the textsfoutdated selected state S ̂ i.
As expected, many of the genes with high positive module membership measure are known to participate in ES cell regulation (Zic3, Mkrn1, Phc1, Esrrb, Jarid2, Nodal, Jarid1b, Tgif1, Utf1, Hells, and Rest) [ 20, 55, 56].
The number o of positive mutual memberships M S i u A j ⋒ M A j S i u define the outcome of an aggregation session as illustrated in Algorithm 3. Algorithm 3 The second level check.
If there are no positive mutual memberships detected (o=0 in line 8 and 9 of Algorithm 3), there is no positive M A j S i u membership (no selected state aggregated before from D i ) and/or there is no positive M S i u A j membership in any bloom filter of the possible states.
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