Using text to build semantic networks for pharmacogenomics

By Adrien Coulet, Nigam H. Shah, Yael Garten, Mark A. Musen, R. B. Altman. 2010

69 citations recorded by the source corpus.

Most pharmacogenomics knowledge is contained in the text of published studies, and is thus not available for automated computation. Natural Language Processing (NLP) techniques for extracting relationships in specific domains often rely on hand-built rules and domain specific ontologies to achieve good performance. In a new and evolving field such as pharmacogenomics (PGx), rules and ontologies may not be available. Recent progress in syntactic NLP parsing in the context of a large corpus of pharmacogenomics text provides new opportunities for automated relationship extraction. We describe an ontology of PGx relationships built starting from a lexicon of key pharmacogenomic entities and a syntactic parse of more than 87 million sentences from 17 million Medline abstracts. We used the syntactic structure of PGx statements to systematically extract commonly occurring relationships and to map them to a common schema. Our extracted relationships have a 70 to 87.7% precision and involve not only key PGx entities such as genes, drugs, and phenotypes (e.g., VKORC1, warfarin, clotting disorder), but also critical entities that are frequently modified by these key entities (e.g., VKORC1 polymorphism, warfarin response, clotting disorder treatment). The result of our analysis is a network of 40,000 relationships between more than 200 entity types with clear semantics. This network is used to guide the curation of PGx knowledge and provide a computable resource for knowledge discovery.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuan Luo
2. Ozlem Uzuner
3. Peter Szolovits
4. Zexian Zeng
5. Tristan Naumann
6. Xiaoyu Li
7. Yu Deng
8. QuanQiu Wang

In Molecular Biology

1. Yuan Luo
2. Ozlem Uzuner
3. Peter Szolovits
4. Zexian Zeng
5. Tristan Naumann
6. Xiaoyu Li
7. Yu Deng
8. Russ B. Altman

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “criteria [195], representing test results for automating diagnosis categorization [108], and building pharmacogenomic semantic networks [58], where the number of nodes is flexi-”
• Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “Many pattern-or parsing-based biomedical named entity recognition and relationship extraction approaches have recently developed [7, 10, 16, 34, 35, 36].”
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “However, there is a gap between the technical advances and the demands from many real-world tasks, including building pharmacogenomics semantic networks [35], extracting clinical trial eligibility criteria [32] and representing test results for automating diagnosis categorization [44,45].”
• DISCOVERY AND EXPLANATION OF DRUG-DRUG INTERACTIONS VIA TEXT MINING (2011, methodology): “8,9 Previous work from our own group 10,11 has established methods for using a syntactical parser to identify and characterize drug-gene relationships.”
• EliXR: an approach to eligibility criteria extraction and representation (2011, background): “12 13 Therefore, we further hypothesize that a syntactic parser integrated with a pattern-mining algorithm can facilitate ef fi cient semantic pattern extraction in clinical research eligibility criteria.”
• Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “semantic networks [40], extracting clinical trial eligibility criteria [36] and representing test results for automating diagnosis categorization [49, 50].”