Predicting treatment relations with semantic patterns over biomedical knowledge graphs

Gokhan Bakal, Ramakanth Kavuluru

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Identifying new potential treatment options (say, medications and procedures) for known medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Even before this step, due to recent advances, in silico or computational approaches are also being employed to identify viable treatment options. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. In this paper, we report preliminary results on predicting treatment relations between biomedical entities purely based on semantic patterns over biomedical knowledge graphs. As such, we refrain from explicitly using NLP, although the knowledge graphs themselves may be built from NLP extractions. Our intuition is fairly straightforward - entities that participate in a treatment relation may be connected using similar path patterns in biomedical knowledge graphs extracted from scientific literature. Using a dataset of treatment relation instances derived from the well known Unified Medical Language System (UMLS), we verify our intuition by employing graph path patterns from a well known knowledge graph as features in machine learned models. We achieve a high recall (92%) but precision, however, decreases from 95% to an acceptable 71% as we go from uniform class distribution to a ten fold increase in negative instances. We also demonstrate models trained with patterns of length ≤ 3 result in statistically significant gains in F-score over those trained with patterns of length ≤ 2. Our results show the potential of exploiting knowledge graphs for relation extraction and we believe this is the first effort to employ graph patterns as features for identifying biomedical relations.

Original languageEnglish
Title of host publicationMining Intelligence and Knowledge Exploration - 3rd International Conference, MIKE 2015, Proceedings
EditorsRajendra Prasath, Anil Kumar Vuppala, T. Kathirvalavakumar
Pages586-596
Number of pages11
DOIs
StatePublished - 2015
Event3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015 - Hyderabad, India
Duration: Dec 9 2015Dec 11 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9468
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015
Country/TerritoryIndia
CityHyderabad
Period12/9/1512/11/15

Bibliographical note

Funding Information:
Thanks to anonymous reviewers for their helpful comments that helped improve the paper. The project described in this paper was supported by the National Center for Advancing Translational Sciences (UL1TR000117). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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