Abstract: A computer-implemented method, computing system and computer-readable medium for determining HER2-low status of a patient using molecular data of the patient includes: (a) receiving digital biological data; (b) processing the digital biological data using a trained multi-stage machine learning architecture; (c) generating a digital HER2-low status report corresponding to the patient; and (d) causing the digital HER2-low status report to be displayed. A computer-implemented method, computing system and computer-readable medium for training a model architecture to determine HER2-low status of a patient using molecular data of the patient includes: (a) receiving training digital biological data; (b) initializing a machine learning model; (c) processing the plurality of molecular signatures using the machine learning model to generate a trained machine learning model; and (d) storing the trained machine learning.

Abstract: A implemented method, computing system and computer-readable medium for stratifying patient cancer risk using molecular data includes receiving molecular data; processing the molecular data using a machine learning model; and generating a matched treatment strategy for the patient based upon the patient's molecular data risk. A computer-implemented method, computing system and computer-readable medium for training a machine learning model to stratify patient cancer risk using molecular data includes receiving a patient training dataset, and a reference training dataset; selecting a cohort of patients; selecting a small subset of genes using univariate selection; generating a corrected reference training dataset; selecting a smaller subset of genes using multivariate selection; training a survival model; and (g) selecting a decision threshold to identify a patient population.

Abstract: The disclosure provides methods and systems for predicting an effect of a pharmaceutical agent in a test subject of a first species. Information about the test subject is input into a multi-task model comprising a plurality of parameters. The model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs including a predicted effect of the pharmaceutical agent in the test subject and, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification. The information about the test subject includes a plurality of abundance values including, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.

Abstract: Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

Abstract: Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: Systems and methods are provided for determining a cancer type of a somatic tissue in a subject. A first plurality of sequence reads is obtained from a plurality of RNA molecules in a biopsy of the subject. A first set of sequence features comprising relative miRNA abundance values of genes is determined from the first plurality of sequence reads. Sequence features are applied to a classification model trained to distinguish between each cancer type in a set of at least 50 cancer types, thus determining the cancer type of the somatic tissue in the subject. The classification model provides an indication that the somatic tissue is or is not a respective cancer type, and the set of cancer types includes at least two cancer types from one or more classes of cancer selected from the group consisting of hematological cancers, squamous cancers, endometrial cancers, sarcoma cancers, and neuroendocrine cancers.

Abstract: Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.