Abstract: Methods of selecting a therapeutic agent to treat a cancer in a subject comprising receiving a plurality of protein data element values representing levels of the proteins in a biological sample from the subject, classifying each protein as a member of a process class, for each process class calculating a process score that represents the prominence of the process class to cancer treatment resistance and selecting at least one therapeutic agent that targets a selected process based on the process score. Systems for selecting a therapeutic agent are also provided.

Abstract: Methods of selecting a therapeutic agent to treat a cancer in a subject comprising receiving a plurality of protein data element values representing levels of the proteins in a biological sample from the subject, classifying each protein as a member of a process class, for each process class calculating a process score that represents the prominence of the process class to cancer treatment resistance and selecting at least one therapeutic agent that targets a selected process based on the process score. Systems for selecting a therapeutic agent are also provided.

Abstract: Methods of selecting a therapeutic agent to treat a cancer in a subject comprising receiving a plurality of protein data element values representing levels of the proteins in a biological sample from the subject, classifying each protein as a member of a process class, for each process class calculating a process score that represents the prominence of the process class to cancer treatment resistance and selecting at least one therapeutic agent that targets a selected process based on the process score. Systems for selecting a therapeutic agent are also provided.

Abstract: A method comprising receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.

Abstract: A computerized system and method for planning a medical treatment for an individual under specific medical condition comprises a data input utility configured for receiving input data, and a data processor. The input data includes first input data comprising medical data of a specific individual, and second input data comprising data indicative of at least one endpoint of treatment. The data processor is configured for utilizing the medical data of the specific individual and the data indicative of the at least one endpoint and processing data indicative of disease progression models, each disease progression model corresponding to a treatment plan comprising one or more predetermined treatment protocols for treating the specific medical condition. The data processor generates output data indicative of a treatment effect on the individual with respect to each of the treatment plans and at least one endpoint to evaluate the treatment plans.

Abstract: A computerized system and method for planning a medical treatment for an individual under specific medical condition comprises a data input utility configured for receiving input data, and a data processor. The input data includes first input data comprising medical data of a specific individual, and second input data comprising data indicative of at least one endpoint of treatment. The data processor is configured for utilizing the medical data of the specific individual and the data indicative of the at least one endpoint and processing data indicative of disease progression models, each disease progression model corresponding to a treatment plan comprising one or more predetermined treatment protocols for treating the specific medical condition. The data processor generates output data indicative of a treatment effect on the individual with respect to each of the treatment plans and at least one endpoint to evaluate the treatment plans.

Abstract: The present disclosure relates to a liquid electrophotographic ink composition comprising clay platelets. Also disclosed herein is a method for electrophotographic printing, and a print substrate.