Model-based featurization and classification

Abstract

In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state. 20 26 20 61 91 17 J ul 2 02 6 2 0 2 6 2 0 6 1 9 1 1 7 J u l 2 0 2 6

Images