Abstract: The present application provides methods for the identification of somatic genome alterations with functional impact in the genome of a tumor. In several embodiments, the methods comprise generating a bipartite causal Bayesian network with maximal posterior probability including causal edges pointing from genes including somatic mutations and somatic copy number alterations in the genome of the tumor to genes having differential expression in the tumor. The methods can be used, for example, to identify driver somatic genome alterations in the genome of a tumor.

Abstract: This document describes machine learning techniques for detecting reduced blood flow conditions, such as ischemia and stroke, in real-time based on electroencephalography (EEG) signals. In one aspect, a method includes receiving patient data that includes a set of EEG signals generated by an EEG device measuring brain function of the patient. A set of feature values are generated for the patient using the EEG signals. The feature values are provided as input to a trained machine learning model that has been trained to detect reduced blood flow conditions of patients. An indication of whether the patient has the reduced blood flow condition is received as a machine learning output of the trained machine learning model. The indication of whether the patient has the reduced blood flow condition is provided.

Abstract: The present application provides methods for the identification of somatic genome alterations with functional impact in the genome of a tumor. In several embodiments, the methods comprise generating a bipartite causal Bayesian network with maximal posterior probability including causal edges pointing from genes including somatic mutations and somatic copy number alterations in the genome of the tumor to genes having differential expression in the tumor. The methods can be used, for example, to identify driver somatic genome alterations in the genome of a tumor.