Abstract: Described here are techniques for identifying risk of primary graft dysfunction (PGD) of a subject. The disclosed method can include collecting serum of the subject, measuring a level of a PGD marker from the serum, wherein the PGD marker comprises plasma kallikrein (KLKB1), providing a PGD risk value that is quantified based on the level of the PGD marker using an adaptive Monte Carlo cross-validation (MCCV) model, and identifying the risk of PGD based on the PGD risk value.

Abstract: System and methods for diagnosing nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) in patients are disclosed. The system can comprise one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more of processors including instructions operable when executed by one or more of the processor. The system can be configured to select at least one patient with a risk indicator using an electronic health record (EHR) database, determine that the at least one patient fails to meet exclusion criteria, and display the at least one patient in response to the determination. The risk indicator can be associated with NAFLD and/or NASH. Methods for diagnosing NAFLD/NASH in patients are disclosed are also provided.

Abstract: A system for collecting and distributing a digital audiovisual item captured by a sensor using a blockchain server is disclosed. The system can comprise a security module that is coupled to the sensor and adapted to generate a private cryptographic key. The system can further include a blockchain-enabled hardware, coupled to the sensor and the security module. The hardware can generate a set of original hashes corresponding to the captured digital audiovisual item and an identifier having an address to the original hashes, embed information corresponding to the identifier in the captured digital audiovisual item to create a blockchain identified audiovisual item, and post the set of original hashes to a public blockchain using the private cryptographic key. Methods for collecting and distributing a digital audiovisual item captured by a sensor using a blockchain server are also provided.

Abstract: System and methods for predicting success rates of clinical trials are disclosed. The system can comprise one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more of processors including instructions operable when executed by one or more of the processor. The system is configured to cause the system to construct a training set using a data source, a performance score and a robustness score of the training set based on selected features, a random forest model based on the calculated performance and robustness scores; and calculate a toxicity score of the pharmaceuticals by applying the random forest model to a genome which is affected by the pharmaceuticals. Methods for predicting success rates of clinical trials and pharmaceuticals are also provided.

Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.

Abstract: Techniques for predicting synthetic lethality in a first species using experimentally derived interactions from at least a second species. An example method can include generating a first biological network for the first species and a second biological network for the second species that include node information representing genes and edge information representing physical interactions between gene-protein products. The method can include determining and normalizing one or more network parameters to permit comparisons between the first and second biological networks. The method can further include training a synthetic lethality model with the experimentally derived synthetic lethality data and applying the synthetic lethality model to the first biological network to predict one or more synthetic lethality pairs.

Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.

Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.