Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2009-02-10
2010-06-29
Vincent, David R (Department: 2129)
Data processing: artificial intelligence
Machine learning
C435S006120, C702S019000
Reexamination Certificate
active
07747547
ABSTRACT:
Apparatus, systems and methods for determining, for each respective phenotypic characterization in a set of {T1, . . . , Tk} characterizations, that a test specimen has the respective characterization are provided. A pairwise probability function gpq(X, Wpq), for a phenotypic pair (Tp, Tq) in {T1, . . . , Tk} is learned using a training population. Wpqis a set of parameters derived from Y for (Tp, Tq) by substituting each Y1in Y into gpq(X, Wpq), as X, where Yiis the set of cellular constituent abundance values from sample i in the training population exhibiting Tpor Tq. The learning step is repeated for each (Tp, Tq) in {T1. . . , Tk}, thereby deriving pairwise probability functions G={g1,2(X, W1,2), . . . , gk-1, k(X, Wk-1, k)}. Pairwise probability values P={p1,2, . . . , pk-1, k} are computed, where each ppqis equal to gpq(Z, Wpq) in G, the probability that the test specimen has Tpand not Tq, where Z is cellular constituent abundance values of the test specimen.
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Anderson Glenda G.
Buturovic Ljubomir J.
Jones Day
Pathwork Diagnostics, Inc.
Vincent David R
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