Abstract: Systems and methods that receive as input microscopy images, extract features, and apply layers of processing units to compute one or more sets of cellular phenotype features, particularly antibodies, corresponding to cellular densities and/or fluorescence measured under different conditions. The system is a machine learning architecture having, in one aspect, a deep neural network, typically a convolutional neural network. The deep neural network can be trained and tested directly on raw microscopy images. The system computes class specific feature maps for every phenotype variable using a deep neural network. The system produces predictions for one or more reference antibody variables based on microscopy images within populations of cells.

Abstract: Disclosed herein are antibodies that specifically bind and inhibit the action of the IL-6 family member LIF that are useful in the treatment of cancer. Also disclosed herein are uses of said antibodies for the treatment of cancer.

Abstract: Described herein are methods of treating cancer comprising administering to the individual (a) a PD-1 axis inhibitor; and (b) an inhibitor of periostin.

Abstract: The present invention provides humanized antibodies and antigen binding fragments thereof that bind to human poliovirus (PVR). The antibodies are useful in the treatment of tumors or cancers.

Abstract: Described herein are anti-FLT3 agonistic antibodies. Such agonistic antibodies are useful for the expansion of dendritic cells and the treatment of cancer.

Abstract: Described herein are antibodies that block the function of periostin. Also described herein are their uses in treating cancer and modifying tumor immune properties.

Abstract: Described herein are assays useful for the quantitation of total LIF from biological samples.