The PheneBank Project

PheneBank aims at automatic extraction and validation of a database of human phenotype-disease associations in the scientific literature. This package provides code, data, and models for the following three purposes:

1. Named Entity Recognition (Tagging)

The model is trained to support 9 categories of entities:

• Phenotype
• Disease
• Anatomy
• Cell
• Cell_line
• GPR
• Gene_variant
• Molecule
• Pathway

2. Harmonisation (Grounding)

Map an entity to its corresponding concept in any of the following 5 ontologies:

• SNOMED (Phenotype, Disease, GPR, Anatomy, Molecule, Cell, Cell_line, Gene_variant)
• HPO (Phenotype, Disease)
• MESH (Phenotype, Disease)
• FMA (Anatomy)
• PRO (GPR)

3. Tagging and Grounding

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Given an input text, extract its entities and map each to its corresponding concept in the ontologies (a pipeline containing both previous stages).

Online demo

• To be activated soon!

Data

Download the followings:

• embeddings.zip (around 1.1GB)
• data.zip (around 200MB)

Getting Started:

To get started with the pipeline, first obtain the required data and decompress them in the project directory. Then, import pipeline into your project:

from pipeline import pipe
pp = pipe()
input_text = "Risk factors for recurrent respiratory infections in preschool children in China."

Find entities in an input text:

pp.tag(input_text)

The output will look like the following (formatted for clarity). Lists of tuples, one tuple per sentence. Each tuple contains two lists: words and their corresponding tags.

[
(['Risk', 'factors', 'for', 'recurrent', 'respiratory', 'infections', 'in', 'China.'],
['O', 'O', 'O', 'B-Phenotype', 'I-Phenotype', 'I-Phenotype', 'O', 'O'])
]

Find entities in the text and harmonise (map) them to their corresponding ontologies:

pp.tag_harmonise(input_text)

The output will have each sentence as a list of tuples. Each tuple has three parts: word, tag (Null if not an entity), (the list of) corresponding concept IDs ([] if no mapping was found).

[
[
('Risk', 'Null', []),
('factors', 'Null', []),
('for', 'Null', []),
('recurrent respiratory infections', 'Phenotype', [('HP:0002205', 1.0)]),
('in', 'Null', []),
('China', 'Null', [])
]
]

Updaing ontology embeddings:

1. Place the new ontology file (eg, hp.obo) under the data directory.
2. Fix the corresponding path in utils/project_config.py.
3. Use the ontology_embedding.py script under grounding to create a new semantic embedding.

You can use the following command in the "embeddings" directory to binarise the ontology embedding:

$ ./convertvec txt2bin [embedding.txt] [embedding.bin]

(convertvec script from https://github.com/marekrei/convertvec)

Dependencies

The tagging stage relies on Anago, a Bidirectional LSTM-CRF for Sequence Labeling: https://github.com/Hironsan/anago

Citation

M.T. Pilehvar, D. Smedley, A. Bernard, and N. Collier: PheneBank: a literature-based database of phenotypes. Bioinformatics, Volume 38, Issue 4, 2022.