The University of North Carolina at Chapel Hill

University of Utah Research Foundation

Washington University

British Columbia Cancer Agency Branch

Andrew Nobel

Charles M Perou

Philip Seth Bernard

Joel S Parker

Kevin Jeffay

Torsten O Nielsen

Frank Donelson Smith

Elaine R Mardis

James Stephen Marron

Maggie Cheang

Felix Hernandez-Campos

Philip S Bernard

James Marron

Torsten Nielsen

Methods for classifying and for evaluating the prognosis of a subject having breast cancer are provided. The methods include prediction of breast cancer subtype using a supervised algorithm trained to stratify subjects on the basis of breast cancer intrinsic subtype. The prediction model is based on the gene expression profile of the intrinsic genes listed in Table 1. This prediction model can be used to accurately predict the intrinsic subtype of a subject diagnosed with or suspected of having breast cancer. Further provided are compositions and methods for predicting outcome or response to therapy of a subject diagnosed with or suspected of having breast cancer. These methods are useful for guiding or determining treatment options for a subject afflicted with breast cancer. Methods of the invention further include means for evaluating gene expression profiles, including microarrays and quantitative polymerase chain reaction assays, as well as kits comprising reagents for practicing the methods of the invention.

Method of classifying a breast cancer instrinsic subtype

Methods, systems, and computer program products are disclosed for determining application-level traffic characteristics in a network based on transport and network layer header information. Transport and network layer header information is collected from packet traffic in a network. Packets are classified to different connections based on the transport and network layer header information. Each connection is modeled using an abstract syntax for characterizing bidirectional interactions between endpoints of each connection and delays between the interactions. Application-level characteristics of the packet traffic are determined based on the modeled connections. Simulated traffic that models application-level traffic behavior in a real network may also be generated by simulating traffic connections based on the modeled connections.

Methods, systems, and computer program products for modeling and simulating application-level traffic characteristics in a network based on transport and network layer header information

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Chapel Hill, NC, United States of America

Andrew Nobel