Machine learning implementation for multi-analyte assay development and testing

A system of screening an individual for lung cancer, the system comprising:

• one or more measurement devices configured to assay a plurality of classes of molecules in a biological sample from the individual using a plurality of assays, wherein the assaying provides a plurality of sets of measured values representative of the plurality of classes of molecules, the biological sample being whole blood, plasma, or serum, wherein the plurality of classes of molecules includes a first class of nucleic acids endogenous to the individual and a second class of polyamino acids endogenous to the individual, wherein the first class of nucleic acids is cell-free DNA (cfDNA), wherein a first assay is applied to the cfDNA molecules to obtain a first set of measured values, and wherein a second assay is applied to the polyamino acids to obtain a second set of measured values, and wherein the first assay applied to the cfDNA molecules includes methylation sequencing;
• and
• a processor and a non-transitory memory, the memory comprising computer program instructions that when executed by the processor, cause the processor to perform:
• identifying a set of features corresponding to properties of each of the plurality of classes of molecules to be input to a machine learning model;
• preparing a feature vector of feature values from the plurality of sets of measured values representative of the plurality of classes of molecules, each feature value corresponding to a feature of the set of features and including one or more measured values, wherein the feature vector includes at least one feature value obtained using each set of the plurality of sets of measured values representative of the plurality of classes of molecules;
• loading, into a memory of a computer system, the machine learning model, the machine learning model trained using training vectors obtained from training biological samples, a first subset of the training biological samples identified as having the lung cancer and a second subset of the training biological samples identified as not having the lung cancer;
• and
• inputting the feature vector into the machine learning model to obtain an output classification of whether the individual has the lung cancer.

A non-transitory computer readable storage medium comprising computer program instruction that, when executed by a computer processor, cause the computer processor to perform:

• a) receiving measured values resulting from assaying a plurality of classes of molecules in a biological sample from a individual using a plurality of assays, wherein the assaying provides a plurality of sets of measured values representative of the plurality of classes of molecules, the biological sample being whole blood, plasma, or serum, wherein the plurality of classes of molecules includes a first class of nucleic acids endogenous to the individual and a second class of polyamino acids endogenous to the individual, wherein the first class of nucleic acids is cell-free DNA (cfDNA), wherein a first assay is applied to the cfDNA molecules to obtain a first set of measured values, and wherein a second assay is applied to the polyamino acids to obtain a second set of measured values, and wherein the first assay applied to the cfDNA molecules includes methylation sequencing;
• b) identifying a set of features corresponding to properties of each of the plurality of classes of molecules to be input to a machine learning model;
• c) preparing a feature vector of feature values from the plurality of sets of measured values representative of the plurality of classes of molecules, each feature value corresponding to a feature of the set of features and including one or more measured values, wherein the feature vector includes at least one feature value obtained using each set of the plurality of sets of measured values representative of the plurality of classes of molecules;
• d) loading, into a memory of a computer system, the machine learning model, the machine learning model trained using training vectors obtained from training biological samples, a first subset of the training biological samples identified as having a lung cancer and a second subset of the training biological samples identified as not having the lung cancer;
• and
• e) inputting the feature vector into the machine learning model to obtain an output classification of whether the individual has the lung cancer.