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: Described herein are techniques of using a hybrid analog-digital processor to perform matrix operations. The hybrid analog-digital may store digital values in memory encoded in a low bit number format. The hybrid analog-digital processor may perform, using an analog processor, a matrix operation to obtain output(s). The output(s) may be encoded in the number format. The hybrid analog-digital processor may determine, using the output(s), an unbiased estimate of a matrix operation result. The hybrid analog-digital processor may store, in the memory, the unbiased estimate of the matrix operation result encoded in the number format.

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 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.