Abstract: A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.

Abstract: Omics patient data are analyzed using sequences or diff objects of tumor and matched normal tissue to identify patient and disease specific mutations, using transcriptomic data to identify expression levels of the mutated genes, and pathway analysis based on the so obtained omic data to identify specific pathway characteristics for the diseased tissue. Most notably, many different tumors have shared pathway characteristics, and identification of a pathway characteristic of a tumor may thus indicate effective treatment options ordinarily not considered when tumor analysis is based on anatomical tumor type only.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Various compounds, compositions, and methods for inhibition of Rit1 are presented. In especially preferred aspects, contemplated compounds and compositions are suitable for treatment of cancers and other diseases associated with Rit1 signaling.

Abstract: Contemplated cancer treatments comprise recursive analysis of patient-, cancer-, and location-specific neoepitopes from various biopsy sites of a patient after treatment or between successive rounds of immunotherapy and/or chemotherapy to inform further immunotherapy. Recursive analysis preferably includes various neoepitope attributes to so identify treatment relevant neoepitopes.

Abstract: Contemplated antiviral/cancer treatments comprise analysis of neoepitopes from viral DNA that has integrated into the host genome, and design of immunotherapeutic agents against such neoepitopes.

Abstract: Specific mutations of FGFR3 (S249C) and of TP53 (V272M) are identified as being characteristic of breast cancer, and of having utility in diagnosis and prognosis of an individual with breast cancer. Systems and methods useful for identification of such mutations are also presented.

Abstract: Contemplated systems and methods allow for prediction of chemotherapy outcome for patients diagnosed with high-grade bladder cancer. In particularly preferred aspects, the prediction is performed using a model based on machine learning wherein the model has a minimum predetermined accuracy gain and wherein a thusly identified model provides the identity and weight factors for omics data used in the outcome prediction.