Abstract: Systems and methods are provided for identifying a set of highly-correlated genes for use in classifying both a first type of biological sample and a second type of biological sample as to a phenotypic characterization, where the first type of biological sample and the second type of biological sample are a sample type selected from the group consisting of a fresh biological sample, a frozen biological sample, a biological sample that has been preserved with a non-crosslinking preservative, and a fixed biological sample that has been fixed with a crosslinking agent, and where the first type of biological sample and the second type of biological sample are sample types that are not the same sample type. The invention also relates to computer systems and methods for use in training a classifier using the highly-correlated genes, and using the trained classifier to classify biological samples that are of the same sample type as the first type of biological sample or the second type of biological sample.

Abstract: The present invention relates to a method and system for standardizing a test microarray dataset. The method requires obtaining a test microarray dataset comprising abundance values for a first plurality of cellular constituents, providing a standardization data structure associated with values of central tendency and identifies, and applying the standardization data structure to the test microarray dataset using a mathematical transformation in order to produce a standardized test microarray dataset. This change has been made for purposes of compact prosecution and to better embrace the allowable subject matter.

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. Wpq is a set of parameters derived from Y for (Tp, Tq) by substituting each Y1 in Y into gpq(X, Wpq), as X, where Yi is the set of cellular constituent abundance values from sample i in the training population exhibiting Tp or 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 ppq is equal to gpq(Z, Wpq) in G, the probability that the test specimen has Tp and not Tq, where Z is cellular constituent abundance values of the test specimen.