Abstract: Introduced here is an approach to further refining an initial set of target locations that can serve as inputs to machine learning mechanisms. These target locations may refer to unique molecular positions in a reference human genome and/or mutations thereof that are diagnostically relevant for a given cancer type. The system can implement a refinement mechanism to account for unnecessary or problematic data, such as consecutive/overlapping patterns, non-uniform read counts, insufficient data quality, internal processing noises, and/or insufficient data counts.

Abstract: Introduced here is an approach to further refining an initial set of target locations that can serve as inputs to machine learning mechanisms. These target locations may refer to unique molecular positions in a reference human genome and/or mutations thereof that are diagnostically relevant for a given cancer type. The system can implement a refinement mechanism to account for unnecessary or problematic data, such as consecutive/overlapping patterns, non-uniform read counts, insufficient data quality, internal processing noises, and/or insufficient data counts.

Abstract: Introduced here is an approach to further refining a set of locations that can serve as inputs to a machine learning algorithm. These locations may refer to unique molecular positions in a reference human genome and/or unique mutations thereof that are relevant in diagnosing cancer. A computing system can determine the diagnostic relevance of each location and then discard some of the less diagnostically relevant locations.

Abstract: Introduced here is an approach to detect existence of cancer or a likely onset of cancer based on analyzing DNA data derived from unbounded samples that are not limited to specific locations of a patient’s body or specific types of cancers. One or more machine learning models may be developed using targeted patterns in the human genome. The machine learning models may be trained to analyze and detect mutation patterns characteristic of one or more cancers. The trained models may be used to analyze the unbounded samples to assess the existence cancer or the proximity to the onset of cancer based on identifying mutation patterns in the patient DNA to the patterns characteristic of the one or more cancers.

Abstract: Introduced here is an approach to training a machine learning model to classify a patient amongst multiple cancer types using sets of locations that indicate where mutations typically occur for those multiple cancer types. Upon being applied to genetic information associated with a patient whose health state is unknown, the machine learning model can produce, as input, values that indicate the likelihood of the patient having each of the multiple cancer types. Also introduced here is an approach in which diagnoses are predicted in an improved manner through the application of different models in “tiers” or “stages.” The approach may involve applying a set of multiple models to the genetic information of an individual in order to ascertain the health of the individual, and each of the multiple models can be used to indicate whether the next model in the set should be applied.

Abstract: Introduced here is an approach to training a machine learning model to classify a patient amongst multiple cancer types using sets of locations that indicate where mutations typically occur for those multiple cancer types. Upon being applied to genetic information associated with a patient whose health state is unknown, the machine learning model can produce, as input, values that indicate the likelihood of the patient having each of the multiple cancer types. Also introduced here is an approach in which diagnoses are predicted in an improved manner through the application of different models in “tiers” or “stages.” The approach may involve applying a set of multiple models to the genetic information of an individual in order to ascertain the health of the individual, and each of the multiple models can be used to indicate whether the next model in the set should be applied.