PubTator

Automated concept annotation for biomedical full text articles

Chih-Hsuan Wei* Alexis Allot* Zhiyong Lu (PI)

https://www.ncbi.nlm.nih.gov/research/pubtator/

Robert Leaman
Biomedical Literature Textmining

INTRODUCTION

PROBLEM: 

• Millions of biomedical publications in PubMed.

• Knowledge present in unstructured form as 

natural language.

Can not be easily :

• Processed by computers

• Integrated into curated databases
SOLUTION: 

Automated text mining allows to easily access and extract 

knowledge within the biomedical literature. 

1. For downstream text mining applications

• gene prioritization

• genetic disease analysis

• literature-based knowledge discovery

2. For faster biocuration

Ex: curating a database (such as UniProt)

Biomedical Literature Textmining

INTRODUCTION
Text Mining Tools

INTRODUCTION

Chih-Hsuan Wei, Hung-Yu Kao, Zhiyong Lu. (2015) GNormPlus: An integrative 

approach for tagging genes, gene families, and protein domains. Biomed Res Int.

Robert Leaman, Zhiyong Lu. (2016) TaggerOne: joint named entity recognition and 

normalization with semi-Markov Model. Bioinformatics.

Chih-Hsuan Wei, Lon Phan, Juliana Feltz, Rama Maiti, Tim Hefferon, Zhiyong Lu. 

(2017) tmVar 2.0: integrating genomic variant information from literature with 

dbSNP and ClinVar for precision medicine. Bioinformatics.

Chih-Hsuan Wei, Hung-Yu Kao, Zhiyong Lu. (2012) SR4GN: a species recognition 

software tool for gene normalization. PLoS One.

GNormPlus

SR4GN

tmVar 2.0

TaggerOne

Our team has developed many dedicated tools.
SOLUTION: Web-based tools can simplify distributing 

results from text mining systems to a wide range of 

users:

- No installation or maintenance

- No infrastructure requirement

Biomedical Literature Textmining

INTRODUCTION

C:\ _ PROBLEM: Text-mining command line tools 

require bioinformatics expertise to execute 

them.
PubTator is a Web-based system providing automatic annotations of biomedical concepts such as 

genes and mutations in PubMed abstracts and PMC full-text articles.

FULL-TEXT ARTICLES

PubTator includes the full-text articles 

in the PMC Open Access subset (nearly 

3 million) in addition to the 30+ million 

abstracts in PubMed.

DEEP LEARNING

Cutting-edge machine learning and 

deep learning techniques are applied 

to concept disambiguation for 

improved accuracy.

ALWAYS UP-TO-DATE

PubTator adds new articles every day to

always keep in sync with PubMed and

PMC.

What is PubTator ?

WEBSITE

PubTator supports six concept types: genes/proteins, genetic variants, diseases, chemicals and cell lines
Preprocessing Pipeline

PREPROCESSING

PubMed

PMC

TaggerOne

Disease

tmVar 2.0

Mutation

GNormPlus

Gene

SR4GN

TaggerOne

Chemical

Species

TaggerOne

CellLine

Disambiguation

Entities Extraction

Multi-formats 

generation
Semantic Search

Leverages PubTator annotations to find all 

publications mentioning an entity, regardless of 

which entity name the author uses

ESR1 vs estrogen receptor

Features

WEBSITE
In-Document Search

A menu displays a list of 

bioentities in a publication, 

allowing users to easily navigate

to entities of interest.

Features

WEBSITE
Free Access

PubTator Central data is free and can be 

accessed :

• interactively through a web browser

• programmatically via RESTful API

• downloaded in bulk via FTP

Features

WEBSITE

C:\ _
Collections

Articles can be organized into collections, and 

then be viewed or downloaded together. 

Articles may be added by:

• Selecting specific publications

• Entering a query

• Entering a list of PMIDs

Features

WEBSITE
(Future proof) Architecture

WEBSITE

Django Server MongoDB

Sterling

Bethesda

Sterling

Bethesda

Pure JSON API

Potential clients : 

Smartphone apps, 

Alexa / Google 

Home ...

Scripts, 

Programs

Web App

(Angular)

Clients

Exploit nonrelational data to 

increase throughput
USAGE

Usage 744M

Total API hits

USE CASE 1 : PRIORITIZING PROTEINS ASSOCIATED

WITH GENETIC MUTATIONS IN CANCERS

USE CASE 2 : UNIPROT DATABASE CURATION

“With the assistance of the PubTator textmining tool, we tagged more than 10 000 

articles to assess the ratio of papers 

relevant for curation.”

Poux S, ArighiCN, Magrane M, et al. On expert curation and scalability: 

UniProtKB/Swiss-Prot as a case study. Bioinformatics. 2017;

“The phrase “mutation cancer” was used as 

the search term to retrieve the commonly 

mutated genes tagged by PubTator. The 

proteins co-published with each of the 

identified genetic mutations were retrieved, 

respectively.”

Yu KH, Lee TM, Wang CS, et al. Systematic Protein Prioritization for Targeted 

Proteomics Studies through Literature Mining. J Proteome Res. 2018;
https://www.ncbi.nlm.nih.gov/research/pubtator/

CONCLUSIONS

Conclusions

PubTator is a web-based system for automated 

concept annotations in PubMed abstracts and 

PMC-TM full text articles

FTP (Updated monthly)

ftp://ftp.ncbi.nlm.nih.gov/pub/lu/PubTatorCentral/

API (Updated daily)

https://www.ncbi.nlm.nih.gov/research/pubtator-api/publications/export/biocxml?full=true&pmids=30375428

Raw Text annotation service

https://www.ncbi.nlm.nih.gov/research/pubtator/api.html

Sample client codes in multiple languages : python, java and perl

https://www.ncbi.nlm.nih.gov/research/pubtator/api.html

This research was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health.