☝️ We moved

This library is not maintained anymore.

We moved the LocText model to the text annotation tool, tagtog:

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LocText

Text-mine the relationship of Proteins <--> Cell Compartments (meaning, protein is/functions in cell compartment).

Run on PubMed abstracts or any string (i.e. including full text).

Publication: LocText: relation extraction of protein localizations to assist database curation

Requirements

Runs on Python >= 3.5.

Non-packaged dependencies (each software has its own dependencies):

• nalaf
• STRING tagger

Install

git clone https://github.com/Rostlab/LocText.git
cd LocText
# We recommend you use python virtualenv: https://pypi.python.org/pypi/virtualenv
pip install -r requirements.txt
python -m loctext.download_data

Run

Sample Script

python run.py --text "GCN2 was constitutively localized to the nucleolus or recruited to the nucleolus by amino acid starvation stress"

You should see something like the following:

# Predicted entities:
Entity(class_id: e_1, offset: 0, text: GCN2, norms: {'n_7': 'Q9P2K8,Q9LX30,Q9FIB4,P15442'})
Entity(class_id: e_2, offset: 41, text: nucleolus, norms: {'n_9': 'GO:0005730'})
Entity(class_id: e_2, offset: 71, text: nucleolus, norms: {'n_9': 'GO:0005730'})

# Predicted relations:
Relation(class_id:"r_5": e1:"Entity(class_id: e_1, offset: 0, text: GCN2, norms: {'n_7': 'Q9P2K8,Q9LX30,Q9FIB4,P15442'})"   <--->   e2:"Entity(class_id: e_2, offset: 41, text: nucleolus, norms: {'n_9': 'GO:0005730'})")
Relation(class_id:"r_5": e1:"Entity(class_id: e_1, offset: 0, text: GCN2, norms: {'n_7': 'Q9P2K8,Q9LX30,Q9FIB4,P15442'})"   <--->   e2:"Entity(class_id: e_2, offset: 71, text: nucleolus, norms: {'n_9': 'GO:0005730'})")

Python API

Full documentation is due. For now:

• Read the example script to know how to instantiate the main classes
• Traverse over the extracted relations as in annotated_corpus.predicted_relations(), which returns objects of type Relation, as defined in nalaf

For any any issue or question with the LocText and nalaf code, please open up an issue in the corresponding repository. Indeed, considerable chunks require refactoring and documentation; don't hesitate to complain ;)

Development

We use pytest for testing.

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To do a quick performance cross-validation of the LocText machine-learning model, execute:

# Use --help for more possible arguments
python loctext/learning/train.py --model D0

In the end, you should see something like:

Run Arguments:
	corpus_percentage = 1.0
	cv_with_test_set = False
	eval_corpus = None
	evaluate_only_on_edges_plausible_relations = False
	evaluation_level = 4
	evaluator = <nalaf.learning.evaluators.DocumentLevelRelationEvaluator object at 0x10802df98>
	feature_generators = LocText
	force_external_corpus_evaluation = False
	k_num_folds = 5
	load_model = None
	model = D0
	predict_entities = []
	save_model = None
	training_corpus = LocText
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	Using libraries versions: numpy == 1.11.2, scipy == 0.18.1, scikit-learn == 0.18.1, spacy == 1.2.0

Training corpus stats:
	#documents: 100
	#relations total: 1345
	#relations prot<-->loc: 550
	#entities: Counter({'e_1': 1393, 'e_2': 558, 'e_3': 277})
	#sentences: 1056
	#instances (edges): 663 -- #P=351 vs. #N=312
	#plausible relations from edges: 351
	#features: 302

# class	tp	fp	fn	fp_ov	fn_ov	e|P	e|R	e|F	e|F_SE	o|P	o|R	o|F	o|F_SE
r_5	214	18	89	0	0	0.9224	0.7063	0.8000	0.0031	0.9224	0.7063	0.8000	0.0031