Abstract: Provided herein are methods and systems for detecting genetic variants from sequencing data. The methods and systems provided herein can be useful for identifying the presence or absence of clinically actionable variants from a sequencing data set and reporting the clinically actionable variants to a user of the methods and systems.

Abstract: Provided herein are methods and systems for detecting genetic variants from sequencing data. The methods and systems provided herein can be useful for identifying the presence or absence of clinically actionable variants from a sequencing data set and reporting the clinically actionable variants to a user of the methods and systems.

Abstract: A computer having a memory stores instructions for receiving data. The data comprises one or more characteristics for each cellular constituent in a plurality of cellular constituents that have been measured in a test organism of a species or a test biological specimen from an organism of the species. The memory further stores instructions for computing a model in a plurality of models, wherein the model is characterized by a model score that represents the likelihood of a biological feature in the test organism or the test biological specimen. Computation of the model comprises determining the model score using one or more characteristics for one or more cellular constituents in the plurality of cellular constituents. The memory also stores instructions for repeating the instructions for computing one or more times, thereby computing the plurality of models. The memory also stores instructions for communicating computed model scores.

Abstract: A computer having a memory stores instructions for receiving data. The data comprises one or more characteristics for each cellular constituent in a plurality of cellular constituents that have been measured in a test organism of a species or a test biological specimen from an organism of the species. The memory further stores instructions for computing a model in a plurality of models, wherein the model is characterized by a model score that represents the likelihood of a biological feature in the test organism or the test biological specimen. Computation of the model comprises determining the model score using one or more characteristics for one or more cellular constituents in the plurality of cellular constituents. The memory also stores instructions for repeating the instructions for computing one or more times, thereby computing the plurality of models. The memory also stores instructions for communicating computed model scores.

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: Methods, computer systems, and computer readable medium for testing a plurality of models in order to classify a biological specimen. A determination is made as to whether a model precondition associated with a model in the plurality of models has been satisfied. The model is skipped when the model precondition has not been satisfied. When the model precondition has been satisfied, the first model is tested by selecting a calculation from the first model. The selected calculation is computed using cellular constituent abundance values from the biological specimen in accordance with a calculation algorithm set forth in the selected model. This selecting and computing is repeated for one or more calculations in the selected model. Then, each of these calculations is aggregated in accordance with an aggregation algorithm associated with the selected model. These steps are repeated for at least one other model in the plurality of models.

Abstract: A computer having a memory stores instructions for receiving data. The data comprises one or more characteristics for each cellular constituent in a plurality of cellular constituents that have been measured in a test organism of a species or a test biological specimen from an organism of the species. The memory further stores instructions for computing a model in a plurality of models, wherein the model is characterized by a model score that represents the likelihood of a biological feature in the test organism or the test biological specimen. Computation of the model comprises determining the model score using one or more characteristics for one or more cellular constituents in the plurality of cellular constituents. The memory also stores instructions for repeating the instructions for computing one or more times, thereby computing the plurality of models. The memory also stores instructions for communicating computed model scores.

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.