# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import xml.etree.ElementTree as ET from typing import Dict, Iterator, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @Article{Bagewadi2014, author={Bagewadi, Shweta and Bobi{\'{c}}, Tamara and Hofmann-Apitius, Martin and Fluck, Juliane and Klinger, Roman}, title={Detecting miRNA Mentions and Relations in Biomedical Literature}, journal={F1000Research}, year={2014}, month={Aug}, day={28}, publisher={F1000Research}, volume={3}, pages={205-205}, keywords={MicroRNAs; corpus; prediction algorithms}, abstract={ INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. RESULTS: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. AVAILABILITY: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html. }, note={26535109[pmid]}, note={PMC4602280[pmcid]}, issn={2046-1402}, url={https://pubmed.ncbi.nlm.nih.gov/26535109}, language={eng} } """ _DATASETNAME = "mirna" _DISPLAYNAME = "miRNA" _DESCRIPTION = """\ The corpus consists of 301 Medline citations. The documents were screened for mentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually annotated. The corpus comprises of two separate files, a train and a test set, coming from 201 and 100 documents respectively. """ _HOMEPAGE = "https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html" _LICENSE = 'Creative Commons Attribution Non Commercial 3.0 Unported' _BASE = "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/miRNA/miRNA-" _URLs = { "source": { "train": _BASE + "Train-Corpus.xml", "test": _BASE + "Test-Corpus.xml", }, "bigbio_kb": { "train": _BASE + "Train-Corpus.xml", "test": _BASE + "Test-Corpus.xml", }, } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class miRNADataset(datasets.GeneratorBasedBuilder): """mirna""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="mirna_source", version=SOURCE_VERSION, description="mirna source schema", schema="source", subset_id="mirna", ), BigBioConfig( name="mirna_bigbio_kb", version=BIGBIO_VERSION, description="mirna BigBio schema", schema="bigbio_kb", subset_id="mirna", ), ] DEFAULT_CONFIG_NAME = "mirna_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "passages": [ { "document_id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Value("string"), "offset": datasets.Value("int32"), "entities": [ { "id": datasets.Value("string"), "offsets": [[datasets.Value("int32")]], "text": [datasets.Value("string")], "type": datasets.Value("string"), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), } ], } ], } ] } ) elif self.config.schema == "bigbio_kb": features = kb_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" my_urls = _URLs[self.config.schema] path_xml_train = dl_manager.download(my_urls["train"]) path_xml_test = dl_manager.download(my_urls["test"]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": path_xml_train, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": path_xml_test, "split": "test", }, ), ] def _get_passages_and_entities(self, d) -> Tuple[List[Dict], List[List[Dict]]]: sentences: List[Dict] = [] entities: List[List[Dict]] = [] relations: List[List[Dict]] = [] text_total_length = 0 po_start = 0 # Get sentences of the document for _, s in enumerate(d): # annotation used only for document indexing if s.attrib["text"] is None or len(s.attrib["text"]) <= 0: continue # annotation used only for document indexing if len(s) <= 0: continue text_total_length += len(s.attrib["text"]) + 1 po_end = po_start + len(s.attrib["text"]) start = po_start dp = { "text": s.attrib["text"], "type": "title" if ".s0" in s.attrib["id"] else "abstract", "offsets": [(po_start, po_end)], "offset": 0, # original offset } po_start = po_end + 1 sentences.append(dp) pe = [] # entities re = [] # relations # For each entity for a in s: # If correspond to a entity if a.tag == "entity": length = len(a.attrib["text"]) if a.attrib["text"] is None or length <= 0: continue # no in-text annotation: only for document indexing if a.attrib["type"] in ["MeSH_Indexing_Chemical", "OTHER"]: continue startOffset, endOffset = a.attrib["charOffset"].split("-") startOffset, endOffset = int(startOffset), int(endOffset) pe.append( { "id": a.attrib["id"], "type": a.attrib["type"], "text": (a.attrib["text"],), "offsets": [(start + startOffset, start + endOffset + 1)], "normalized": [ {"db_name": "miRNA-corpus", "db_id": a.attrib["id"]} ], } ) # If correspond to relation pair elif a.tag == "pair": re.append( { "id": a.attrib["id"], "type": a.attrib["type"], "arg1_id": a.attrib["e1"], "arg2_id": a.attrib["e2"], "normalized": [], } ) entities.append(pe) relations.append(re) return sentences, entities, relations def _generate_examples( self, filepath: str, split: str, ) -> Iterator[Tuple[int, Dict]]: """Yields examples as (key, example) tuples.""" reader = ET.fromstring(open(str(filepath), "r").read()) if self.config.schema == "source": for uid, doc in enumerate(reader): ( sentences, sentences_entities, relations, ) = self._get_passages_and_entities(doc) if ( len(sentences) < 1 or len(sentences_entities) < 1 or len(sentences_entities) != len(sentences) ): continue for p, pe, re in zip(sentences, sentences_entities, relations): p.pop("offsets") # BioC has only start for passages offsets p["document_id"] = doc.attrib["id"] p["entities"] = pe # BioC has per passage entities yield uid, {"passages": sentences} elif self.config.schema == "bigbio_kb": uid = 0 for idx, doc in enumerate(reader): ( sentences, sentences_entities, relations, ) = self._get_passages_and_entities(doc) if ( len(sentences) < 1 or len(sentences_entities) < 1 or len(sentences_entities) != len(sentences) ): continue # global id uid += 1 # unpack per-sentence entities entities = [e for pe in sentences_entities for e in pe] for p in sentences: p.pop("offset") # drop original offset p["text"] = (p["text"],) # text in sentence is Sequence p["id"] = uid uid += 1 for e in entities: e["id"] = uid uid += 1 # unpack per-sentence relations relations = [r for re in relations for r in re] for r in relations: r["id"] = uid uid += 1 yield idx, { "id": uid, "document_id": doc.attrib["id"], "passages": sentences, "entities": entities, "events": [], "coreferences": [], "relations": relations, }