Abstract: In silico tools are used to determine possibly effective therapies for treating a patient's cancer based on patient, drug, and cancer information. Functional assays can be performed on living cancer cells from the patient to evaluate the possibly effective therapies along with subsequent genomic or other more destructive assays to provide additional information from a single sample. Drug, patient, cancer, and outcome information can be recorded and updated iteratively and analyzed using machine learning to identify correlations between various patient, cancer, and drug characteristics and expected outcomes and drug efficacies.

Abstract: In silico tools are used to determine possibly effective therapies for treating a patient's cancer based on patient, drug, and cancer information. Functional assays can be performed on living cancer cells from the patient to evaluate the possibly effective therapies along with subsequent genomic or other more destructive assays to provide additional information from a single sample. Drug, patient, cancer, and outcome information can be recorded and updated iteratively and analyzed using machine learning to identify correlations between various patient, cancer, and drug characteristics and expected outcomes and drug efficacies.

Abstract: Methods of calibration are provided. A method comprises introducing a material with cell-like properties and a known mass into a sensor on a measurement instrument to generate a calibration reading and adjusting an output module of the measurement instrument until the measurement instrument calibrates to the known mass for the material.

Abstract: Methods of calibration are provided. A method comprises introducing a material with cell-like properties and a known mass into a sensor on a measurement instrument to generate a calibration reading and adjusting an output module of the measurement instrument until the measurement instrument calibrates to the known mass for the material.

Abstract: The invention provides methods that use machine learning to discover clinical data patterns that are predictive of disease, such as cancer. Clinical data from across a population is provided as input to a machine learning system. The machine learning system discovers associations in data from a plurality of data sources obtained from a population and correlates the associations to cancer status of patients in the population. The methods may further include providing patient data from an individual and predicting, by the machine learning system, a cancer state (e.g., the presence of cancer and a determination of a stage or progression of the cancer, if present) for the individual when the patient data presents one or more of the discovered associations.

Abstract: In silico tools are used to determine possibly effective therapies for treating a patient's cancer based on patient, drug, and cancer information. Functional assays can be performed on living cancer cells from the patient to evaluate the possibly effective therapies along with subsequent genomic or other more destructive assays to provide additional information from a single sample. Drug, patient, cancer, and outcome information can be recorded and updated iteratively and analyzed using machine learning to identify correlations between various patient, cancer, and drug characteristics and expected outcomes and drug efficacies.