Microsoft Technology Licensing, LLC

Dendreon Pharmaceuticals LLC

Debraj GuhaThakurta

Walter Sun

Vijay Krishna Narayanan

Mayank Shrivastava

Nadeem Sheikh

Gagan Bansal

James B Trager

Konstantin Golyaev

Harini Kandadi

Li-Qun Fan

Amita Surendra Gajewar

Gene expression profiling in patients with mCRPC identifying genes that predict overall survival in response to treatment with sipuleucel-T, the method comprising the steps of (a) determining a gene expression level of a first biomarker; (b) determining a gene expression level of at least one additional biomarker different from the first biomarker; and (c) transforming the expression level of the first biomarker and the at least one additional biomarker into a first score corresponding to a probability of overall survival in response to treatment with sipuleucel-T.

Gene expression markers for predicting overall survival in subjects treated with sipuleucel-T

A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

Predictive modeling across multiple horizons combining time series and external data

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Bellevue, WA, United States of America

Debraj GuhaThakurta