Abstract: Methods for cancer source of origin (CSO) prediction are disclosed to predict CSO characteristics. The CSO prediction may include the affected organ or organ group and tumor biology. The method for training parallel CSO classifiers includes obtaining training samples derived from subjects with known cancer diagnosis, each training sample comprising methylation sequence reads corresponding to nucleic acid fragments in a biological sample collected from each subject and each known cancer signal origin including a known affected organ or organ group a plurality of organs or organ groups and a known tumor biology from a plurality of tumor biology classes. The method includes generating, for each training sample, a feature vector based on the methylation sequence reads.

Abstract: Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.