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.

Abstract: Introduced here is an approach that can be implemented by a computing system to remove kit-specific signals from genetic information to be analyzed, such that cancer presence, progression, or regression can be predicted in an improved manner. The computing system can “preprocess” the genetic information so that diagnoses can be more accurately predicted in a largely, if not entirely, kit-agnostic manner. The computing system may apply one or more models to genetic information as part of an inferencing operation in order to produce one or more outputs, each of which may be indicative of a proposed diagnosis for the corresponding individual. “Preprocessing” could also be performed on the genetic information that is used to train these models, such that the kit-specific signals are removed before a training operation is completed.

Abstract: This disclosure provides methods for treating a subject having small cell lung cancer by determining expression levels of biomarkers highly correlated with a subtype of small cell lung cancer that are sensitive to treatment with pentamidine or a pharmaceutically acceptable salt thereof. The methods are drawn to determining a predictive gene expression profile of a subtype of small cell lung cancer and treating the subject with an effective amount of pentamidine or a pharmaceutically acceptable salt of pentamidine as a chemotherapy agent. The methods generally involve treatment of a subtype of small cell lung cancer predicted to be a responder to pentamidine or a pharmaceutically acceptable salt of pentamidine.