Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: The present invention relates to methods of treating ovarian cancer in a subject by administering to the subject by evaluating the subject's expression levels of specific biomarkers or angiogenic an anti-activin-A compound, such as an anti-activin-A antibody or an activin-A-binding receptor. In some embodiments, at least two compounds are administered to the subject, where the first compound is an anti-activin A compound, and the second compound is a chemotherapeutic compound, for example capecitabine. The invention further relates to methods of identifying subjects for treatment factors.