Many paths lead to discovery : analogical retrieval of cancer therapies This paper addresses the issue of analogical inference, and its potential role as the mediator of new therapeutic discoveries, by using disjunction operators based on quantum connectives to combine many potential reasoning pathways into a single search expression. In it, we extend our previous work in which we developed an approach to analogical retrieval using the Predicationbased Semantic Indexing (PSI) model, which encodes both concepts and the relationships between them in highdimensional vector space. As in our previous work, we leverage the ability of PSI to infer predicate pathways connecting two example concepts, in this case comprising of known therapeutic relationships. For example, given that drug x TREATS disease z, we might infer the predicate pathway drug x INTERACTS WITH gene y ASSOCIATED WITH disease z, and use this pathway to search for drugs related to another disease in similar ways. As biological systems tend to be characterized by networks of relationships, we evaluate the ability of quantuminspired operators to mediate inference and retrieval across multiple relations, by testing the ability of different approaches to recover known therapeutic relationships. In addition, we introduce a novel complex vector based implementation of PSI, based on Platexe2x80x99s Circular Holographic Reduced Representations, which we utilize for all experiments in addition to the binary vector based approach we have applied in our previous research.

Extracting Molecular Entities and Their Interactions from Pathway Figures Based on Deep Learning Many new molecular mechanisms of genomics, pharmacogenomics, immunology, and other fields are reflected in pathway figures and need to be curated for various applications, especially in precision medicine. Current manual curation approaches are inadequate in keeping up with the pace of biomedical literature growth. Compared with textual representations, pathway figures in the biomedical literature often contain more direct representations of the mechanisms. However, no systematic method for curating pathway figures exists in publications due to date. Here, we propose a pathway curation pipeline, which integrates a deep learning model with an optical character recognition method and an image processing strategy to capture the locations, names, and interactions of pathway entities in the figure. Our pipeline was evaluated on the figures from PubMed publications. The results demonstrate that our model can effectively retrieve molecular entities and their interactions from pathway figures at a large scale. The proposed pipeline provides a complementary approach to textmining in biological literature mining. In future work, we will combine our method with textmining tools to enrich extracted information and reconstruct pathway mechanisms more precisely.