In recent years, the number of biomedical publications made freely available through literature archives is steadfastly growing , resulting in a rich source of untapped new knowledge. Most biomedical facts are however  buried in the form of unstructured text, and  their exploitation requires expert-knowledge and time-consuming manual curation of published articles. Hence the development of novel methodologies that can automatically analyze textual sources, extract facts and knowledge, and produce summarized representations that capture the most relevant information is a timely and pressing need. We present INtERAcT, a novel approach to infer interactions between molecular entities extracted from literature using an unsupervised procedure that takes advantage of recent developments in automatic text mining and text analysis. INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules . We show that the proposed metric outperforms other metrics at the task of identifying known molecular interactions reported in publicly available databases for different cancer types. Our findings suggest that INtERAcT may increase our capability to summarize the understanding of a specific disease or biological process using published literature in an automated and unsupervised fashion. 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 readily and efficiently applied to different scientific domains , enabling the automatic reconstruction of domain-specific networks of molecular interactions .
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 .
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made freely available through literature archives is steadfastly growing
has steadfastly grown
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not readily available, but
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hence
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expert-knowledge and
the
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Hence the development of novel methodologies that can automatically analyze textual sources, extract facts and knowledge, and produce summarized representations that capture the most relevant information is a timely and pressing need. We
Here we
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infer interactions between molecular entities extracted from literature using an unsupervised procedure that takes advantage of recent developments in automatic text mining and text analysis. INtERAcT
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
R
acting on the vector space of word representations to estimate
that estimates
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. We show that the proposed metric outperforms other metrics
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
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reported in publicly available databases for different
in all studied
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Our findings suggest that INtERAcT may increase our capability to summarize the understanding of a specific disease or biological process using published literature in an automated and unsupervised fashion.
R
readily
easily
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, enabling the automatic reconstruction of domain-specific networks of molecular interactions
. 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