upload hubscripts/mirna_hub.py to hub from bigbio repo

mirna.py

@@ -0,0 +1,383 @@
+# 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,
+                }