# Advanced End-to-End Relation Extraction with Deep Neural Networks

> **NIH NIH R01** · UNIVERSITY OF KENTUCKY · 2020 · $358,691

## Abstract

ABSTRACT
Relations linking various biomedical entities constitute a crucial resource that enables biomedical data science
applications and knowledge discovery. Relational information spans the translational science spectrum going
from biology (e.g., protein–protein interactions) to translational bioinformatics (e.g., gene–disease associations),
and eventually to clinical care (e.g., drug–drug interactions). Scientists report newly discovered relations in nat-
ural language through peer-reviewed literature and physicians may communicate them in clinical notes. More
recently, patients are also reporting side-effects and adverse events on social media. With exponential growth in
textual data, advances in biomedical natural language processing (BioNLP) methods are gaining prominence for
biomedical relation extraction (BRE) from text. Most current efforts in BRE follow a pipeline approach containing
named entity recognition (NER), entity normalization (EN), and relation classification (RC) as subtasks. They
typically suffer from error snowballing — errors in a component of the pipeline leading to more downstream errors
— resulting in lower performance of the overall BRE system. This situation has lead to evaluation of different
BRE substaks conducted in isolation. In this proposal we make a strong case for strictly end-to-end evaluations
where relations are to be produced from raw text. We propose novel deep neural network architectures that
model BRE in an end-to-end fashion and directly identify relations and corresponding entity spans in a single
pass. We also extend our architectures to n-ary and cross-sentence settings where more than two entities may
need to be linked even as the relation is expressed across multiple sentences. We also propose to create two
new gold standard BRE datasets, one for drug–disease treatment relations and another first of a kind dataset
for combination drug therapies. Our main hypothesis is that our end-to-end extraction models will yield supe-
rior performance when compared with traditional pipelines. We test this through (1). intrinsic evaluations based
on standard performance measures with several gold standard datasets and (2). extrinsic application oriented
assessments of relations extracted with use-cases in information retrieval, question answering, and knowledge
base completion. All software and data developed as part of this project will be made available for public use and
we hope this will foster rigorous end-to-end benchmarking of BRE systems.

## Key facts

- **NIH application ID:** 10052028
- **Project number:** 1R01LM013240-01A1
- **Recipient organization:** UNIVERSITY OF KENTUCKY
- **Principal Investigator:** Venkata Naga Ramakanth Kavuluru
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $358,691
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10052028

## Citation

> US National Institutes of Health, RePORTER application 10052028, Advanced End-to-End Relation Extraction with Deep Neural Networks (1R01LM013240-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10052028. Licensed CC0.

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