Title: INtERAcT: Interaction Network Inference from Vector Representations of Words,
Abstract: In recent years, the number of biomedical publications has steadfastly grown,
resulting in a rich source of untapped new knowledge. Most biomedical facts are
however not readily available, but buried in the form of unstructured text, and
hence their exploitation requires the time-consuming manual curation of
published articles. Here we present INtERAcT, a novel approach to extract
protein-protein interactions from a corpus of biomedical articles related to a
broad range of scientific domains in a completely unsupervised way. INtERAcT
exploits vector representation of words, computed on a corpus of domain
specific knowledge, and implements a new metric that estimates an interaction
score between two molecules in the space where the corresponding words are
embedded. We demonstrate the power of INtERAcT by reconstructing the molecular
pathways associated to 10 different cancer types using a corpus of
disease-specific articles for each cancer type. We evaluate INtERAcT using
STRING database as a benchmark, and show that our metric outperforms currently
adopted approaches for similarity computation at the task of identifying known
molecular interactions in all studied cancer types. Furthermore, our approach
does not require text annotation, manual curation or the definition of semantic
rules based on expert knowledge, and hence it can be easily and efficiently
applied to different scientific domains. Our findings suggest that INtERAcT may
increase our capability to summarize the understanding of a specific disease
using the published literature in an automated and completely unsupervised
fashion.