A deep leanring model to predict named entities, triggers, and nested events from biomedical texts.

• The model and results are reported in our paper:

DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts, Bioinformatics, 2020.

• Based on pre-trained BERT
• Predict nested entities and nested events
• Provide our trained models on the seven biomedical tasks
• Reproduce the results reported in our Bioinformatics paper
• Predict for new data given raw text input or PubMed ID
• Visualize the predicted entities and events on the brat

• DeepEventMine has been trained and evaluated on the following tasks (six BioNLP shared tasks and MLEE).

1. cg: Cancer Genetics (CG), 2013
2. ge11: GENIA Event Extraction (GENIA), 2011
3. ge13: GENIA Event Extraction (GENIA), 2013
4. id: Infectious Diseases (ID), 2011
5. epi: Epigenetics and Post-translational Modifications (EPI), 2011
6. pc: Pathway Curation (PC), 2013
7. mlee: Multi-Level Event Extraction (MLEE)

• Our trained models
• Our scores

• Python 3.6.5
• PyTorch (torch==1.1.0 torchvision==0.3.0, cuda92)

virtualenv -p python3 pytorch-env
source pytorch-env/bin/activate
export CUDA_VISIBLE_DEVICES=0
CUDA_PATH=/usr/local/cuda pip install torch==1.1.0 torchvision==0.3.0

• Install Python packages

sh install.sh

• Download SciBERT BERT model from PyTorch AllenNLP

sh download.sh bert

• Download pre-trained DeepEventMine model on a given task
• [task] = cg (or pc, ge11, epi, etc)

sh download.sh deepeventmine [task]

• To visualize the output using the brat
• Download brat v1.3

sh download.sh brat

• Install brat based on the brat instructions

cd brat/brat-v1.3_Crunchy_Frog/
./install.sh -u
python2 standalone.py

1. Download corpora

• To download the original data sets from BioNLP shared tasks.
• [task] = cg, pc, ge11, etc

sh download.sh bionlp [task]

2. Preprocess data

• Tokenize texts and prepare data for prediction

sh preprocess.sh bionlp

3. Generate configs

• If using GPU: [gpu] = 0, otherwise: [gpu] = -1
• [task] = cg, pc, etc

sh run.sh config [task] [gpu]

1. For development and test sets (given gold entities)

• CG task: [task] = cg
• PC task: [task] = pc
• Similarly for: ge11, ge13, epi, id, mlee

sh run.sh predict [task] gold dev
sh run.sh predict [task] gold test

• Check the output in the path

experiments/[task]/predict-gold-dev/
experiments/[task]/predict-gold-test/

1. Retrieve the original offsets and create zip format

sh run.sh offset [task] gold dev
sh run.sh offset [task] gold test

2. Submit the zipped file to the shared task evaluation sites:

• CG Test
• GE11 Test, GE11 Devel
• GE13 Test, GE13 Devel
• ID Test, ID Devel
• EPI Test, EPI Devel
• PC Test

3. Evaluate events

• Evaluate event prediction for PC and CG tasks on the development sets using the shared task scripts.
• Evaluation options: s (softboundary), p(partialrecursive)

sh run.sh eval [task] gold dev sp

• Abstract

sh pubmed.sh e2e pmid 1370299 cg 0

• Full text

sh pubmed.sh e2e pmcid PMC4353630 cg 0

• Input: PMID: 1370299, PMCID: PMC4353630 (a single PubMed ID to get raw text)
• Model to predict: DeepEventMine trained on cg (Cancer Genetics 2013), (other options: pc, ge11, etc)
• GPU: 0 (if CPU: -1)
• Output: in brat format and brat visualization

T24	Organism 1248 1254	bovine
T25	Gene_or_gene_product 1255 1259	u-PA
T55	Positive_regulation 1107 1116	increased
T57	Localization 1170 1179	migration
T58	Negative_regulation 1260 1267	blocked
...

T23	Gene_or_gene_product 1184 1188	u-PA
T56	Positive_regulation 1157 1166	increases
E9	Positive_regulation:T56 Theme:T23

T26	Gene_or_gene_product 1320 1325	c-src
T62	Gene_expression 1326 1336	expression
E10	Gene_expression:T62 Theme:T26

T61	Positive_regulation 1310 1319	increased
E24	Positive_regulation:T61 Theme:E10

• Given an arbitrary name for your raw text data, for example "my-pubmed"
• Prepare a list of PMID and PMCID in the path

data/my-pubmed/pmid.txt

sh pubmed.sh e2e pmids my-pubmed cg 0

• Given an arbitrary name for your raw text data, for example "my-pubmed"
• Prepare your raw text files in the path

data/my-pubmed/text/PMID-*.txt
data/my-pubmed/text/PMC-*.txt

sh pubmed.sh e2e rawtext my-pubmed cg 0

• Input: your own raw text or PubMed ID
• Output: predicted entities and events in brat format

• Given an arbitrary name for your raw text data, for example "my-pubmed"
• Prepare your own raw text in the following path

data/my-pubmed/text/PMID-*.txt
data/my-pubmed/text/PMC-*.txt

• Or, you can automatically get raw text given PubMed ID or PMC ID

1. PubMed ID list

• In order to get full text given PMC ID, the text should be available in ePub (for our current version).
• Prepare your list of PubMed ID and PMC ID in the path

data/my-pubmed/pmid.txt

• Get text from the PubMed ID

sh pubmed.sh pmids my-pubmed

2. PubMed ID

• You can also get text by directly input a PubMed or PMC ID

sh pubmed.sh pmid 1370299
sh pubmed.sh pmcid PMC4353630

sh pubmed.sh preprocess my-pubmed

1. Generate config

• Generate config for prediction
• The data name to predict: my-pubmed
• The trained model used for predict: cg (or pc, ge11, etc)
• If you use gpu [gpu]=0, otherwise [gpu]=-1

sh pubmed.sh config my-pubmed cg 0

2. Predict

sh pubmed.sh predict my-pubmed

3. Retrieve the original offsets

sh pubmed.sh offset my-pubmed

• Check the output in

experiments/my-pubmed/results/ev-last/my-pubmed-brat

• Copy the predicted data into the brat folder to visualize
• For the raw text prediction:

sh pubmed.sh brat my-pubmed cg

• Or for the shared task

sh run.sh brat [task] gold dev
sh run.sh brat [task] gold test

• The data to visualize is located in

brat/brat-v1.3_Crunchy_Frog/data/my-pubmed-brat
brat/brat-v1.3_Crunchy_Frog/data/[task]-brat

This work is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). This work is also supported by PRISM (Public/Private R&D Investment Strategic Expansion PrograM).

@article{10.1093/bioinformatics/btaa540,
    author = {Trieu, Hai-Long and Tran, Thy Thy and Duong, Khoa N A and Nguyen, Anh and Miwa, Makoto and Ananiadou, Sophia},
    title = "{DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts}",
    journal = {Bioinformatics},
    year = {2020},
    month = {06},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa540},
    url = {https://doi.org/10.1093/bioinformatics/btaa540},
    note = {btaa540},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btaa540/33399046/btaa540.pdf},
}