Abstract: Methods, systems, and kits for the diagnosis, prognosis and the determination of cancer progression of prostate cancer in a subject are disclosed. In particular, the disclosure relates to the use of immune cell-specific gene expression in determining prognosis and identifying individuals in need of treatment for prostate cancer who will be responsive to radiation therapy.

Abstract: In some embodiments, a machine learning model may be accessed and used to generate a likelihood score related to a condition. In some embodiments, pre-computed vectors may be derived from a training dataset used to build the machine learning model, and the pre-computed vectors may be used to generate processed data from target data derived from a target sample. The machine learning model may then be used on the processed data to generate the likelihood score related to the condition. As an example, subsets of the training dataset may be randomly selected, and the pre-computed vectors may be derived from the randomly-selected subsets of the training dataset. The pre-computed vectors may be applied to the target data to generate the processed data. In one use case, for example, the target data may be normalized using the pre-computed vectors.