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""" |
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Post-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior |
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processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its |
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activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of |
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information extraction targeting individual PTM types, there was until recently little effort to address extraction of |
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multiple PTM types at once in a unified framework. |
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""" |
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|
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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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|
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import datasets |
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|
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from .bigbiohub import kb_features |
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from .bigbiohub import BigBioConfig |
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from .bigbiohub import Tasks |
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_LANGUAGES = ['English'] |
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_PUBMED = True |
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_LOCAL = False |
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_CITATION = """\ |
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@inproceedings{ohta-etal-2010-event, |
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title = "Event Extraction for Post-Translational Modifications", |
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author = "Ohta, Tomoko and |
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Pyysalo, Sampo and |
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Miwa, Makoto and |
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Kim, Jin-Dong and |
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Tsujii, Jun{'}ichi", |
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booktitle = "Proceedings of the 2010 Workshop on Biomedical Natural Language Processing", |
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month = jul, |
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year = "2010", |
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address = "Uppsala, Sweden", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/W10-1903", |
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pages = "19--27", |
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} |
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""" |
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_DATASETNAME = "genia_ptm_event_corpus" |
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_DISPLAYNAME = "PTM Events" |
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_DESCRIPTION = """\ |
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Post-translational-modifications (PTM), amino acid modifications of proteins \ |
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after translation, are one of the posterior processes of protein biosynthesis \ |
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for many proteins, and they are critical for determining protein function such \ |
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as its activity state, localization, turnover and interactions with other \ |
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biomolecules. While there have been many studies of information extraction \ |
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targeting individual PTM types, there was until recently little effort to \ |
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address extraction of multiple PTM types at once in a unified framework. |
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""" |
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|
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_HOMEPAGE = "http://www.geniaproject.org/other-corpora/ptm-event-corpus" |
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_LICENSE = 'GENIA Project License for Annotated Corpora' |
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_URLS = { |
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_DATASETNAME: "http://www.geniaproject.org/other-corpora/ptm-event-corpus/post-translational_modifications_training_data.tar.gz?attredirects=0&d=1", |
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} |
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_SUPPORTED_TASKS = [ |
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Tasks.NAMED_ENTITY_RECOGNITION, |
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Tasks.COREFERENCE_RESOLUTION, |
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Tasks.EVENT_EXTRACTION, |
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] |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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class GeniaPtmEventCorpusDataset(datasets.GeneratorBasedBuilder): |
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"""GENIA PTM event corpus.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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BUILDER_CONFIGS = [ |
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BigBioConfig( |
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name="genia_ptm_event_corpus_source", |
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version=SOURCE_VERSION, |
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description="genia_ptm_event_corpus source schema", |
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schema="source", |
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subset_id="genia_ptm_event_corpus", |
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), |
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BigBioConfig( |
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name="genia_ptm_event_corpus_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="genia_ptm_event_corpus BigBio schema", |
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schema="bigbio_kb", |
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subset_id="genia_ptm_event_corpus", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "genia_ptm_event_corpus_source" |
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|
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"text_bound_annotations": [ |
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{ |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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"text": datasets.Sequence(datasets.Value("string")), |
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"type": datasets.Value("string"), |
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"id": datasets.Value("string"), |
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} |
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], |
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"events": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value( |
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"string" |
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), |
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"trigger": datasets.Value("string"), |
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"arguments": [ |
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{ |
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"role": datasets.Value("string"), |
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"ref_id": datasets.Value("string"), |
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} |
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], |
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} |
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], |
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"relations": [ |
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{ |
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"id": datasets.Value("string"), |
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"head": { |
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"ref_id": datasets.Value("string"), |
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"role": datasets.Value("string"), |
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}, |
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"tail": { |
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"ref_id": datasets.Value("string"), |
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"role": datasets.Value("string"), |
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}, |
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"type": datasets.Value("string"), |
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} |
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], |
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"equivalences": [ |
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{ |
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"id": datasets.Value("string"), |
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"ref_ids": datasets.Sequence(datasets.Value("string")), |
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} |
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], |
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}, |
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) |
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elif self.config.schema == "bigbio_kb": |
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features = kb_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=str(_LICENSE), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data_dir": data_dir, |
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}, |
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), |
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] |
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|
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def _generate_examples(self, data_dir) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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for dirpath, _, filenames in os.walk(data_dir): |
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for guid, filename in enumerate(filenames): |
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if filename.endswith(".txt"): |
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txt_file_path = Path(dirpath, filename) |
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if self.config.schema == "source": |
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example = parsing.parse_brat_file( |
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txt_file_path, annotation_file_suffixes=[".a1", ".a2"] |
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) |
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example["id"] = str(guid) |
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for key in ["attributes", "normalizations"]: |
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del example[key] |
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yield guid, example |
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elif self.config.schema == "bigbio_kb": |
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example = parsing.brat_parse_to_bigbio_kb( |
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parsing.parse_brat_file(txt_file_path) |
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) |
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example["id"] = str(guid) |
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yield guid, example |
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