Abstract: A method for classifying data using non-negative matrix factorization can include receiving a population of sample data, generating a first matrix of the amplicon counts per sample data, dividing the first matrix into a product of a second matrix and a third matrix, in the second matrix, determining whether each signature is a long or short fragment per each amplicon count, in the third matrix, determining intensities of each signature per the sample data, and classifying the sample data based on the intensities of each signature. The population can include amplicon counts per sample data. The second matrix can include signatures of short and long DNA fragments and the third matrix can include intensities of each signature of the short and long DNA fragments.

Abstract: This document describes technology that can be used for detecting an etiological factor of a disease in a subject having the disease, training data is received that includes data objects each recording i) a disease label, ii) at least one corresponding mutational signature, and iii) corresponding etiological tags. A first set of features based on single nucleotide mutations and a second set of features based on dinucleotide mutations are generated. A machine learning model is trained on the first set of features and on the second set of features. A classifier is generated that is configured to: operate by receiving a new-genomic-data-object, the new-genomic-data-object specific to the subject having the disease; and generate, from the new-genomic-data-object, a etiological-classification for the new-genomic-data-object, the etiological-classification indicating a corresponding etiological factor that matches one of the etiological tags.

Abstract: This document provides methods and materials for identifying chromosomal anomalies that can be used to identify a mammal as having a disease (e.g., cancer or congenital abnormality). For example, this document provides methods and materials for evaluating sequencing data to identify a mammal as having a disease associated with one or more chromosomal anomalies (e.g., cancer or congenital abnormalities). For example, this document provides methods and materials for evaluating sequencing data that can be used in cancer diagnostics, non-invasive prenatal testing (NIPT), preimplantation genetic diagnosis and evaluation of congenital abnormalities.

Abstract: Provided herein are methods and materials for detecting and/or treating subject (e.g. a human) having cancer. In some embodiments, methods and materials for identifying a subject as having cancer (e.g., a localized cancer) are provided in which the presence of member(s) of two or more classes of biomarkers are detected. In some embodiments, methods and materials for identifying a subject as having cancer (e.g. a localized cancer) are provided in which the presence of member(s) of at least one class of biomarkers and the presence of aneuploidy are detected. In some embodiments, methods described herein provide increased sensitivity and/or specificity in the detection of cancer in a subject (e.g. a human).

Abstract: Provided herein are methods and materials for detecting and/or treating subject (e.g., a human) having cancer. In some embodiments, methods and materials for identifying a subject as having cancer (e.g., a localized cancer) are provided in which the presence of member(s) of two or more classes of biomarkers are detected. In some embodiments, methods and materials for identifying a subject as having cancer (e.g., a localized cancer) are provided in which the presence of member(s) of at least one class of biomarkers and the presence of aneuploidy are detected. In some embodiments, methods described herein provide increased sensitivity and/or specificity in the detection of cancer in a subject (e.g. a human).