The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions

By María Herrero-Zazo, Isabel Segura-Bedmar, Paloma Martínez, Thierry Declerck. Journal of Biomedical Informatics, 2013

463 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

View Paper: https://www.semanticscholar.org/paper/6ea353ada2b89763f58d8068a74b2e6def526948

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Zhiyong Lu
2. Tao Qin
3. C. Lee Giles
4. Alfonso Valencia
5. Q. Wu
6. Donghong Ji
7. Roger A. Sayle
8. Hoifung Poon

In Molecular Biology

1. Zhiyong Lu
2. Robert Leaman
3. Mariana Neves
4. Leon Weber
5. Martin Krallinger
6. Qiao Jin
7. Tao Qin
8. Tolga Can

In Toxicology

1. Donghong Ji

Citations in the corpus

• A study of generative large language model for medical research and healthcare (2023, unknown): “…models including GPT-2 43 , REBEL, REBEL-pt 25 , and BioGPT 18 on three biomedical tasks for end-to-end relation extraction using three benchmark datasets including drug-drug interaction 44 (DDI), BioCreative V chemical-disease relation 45 (BC5CDR), and drug-target interaction 46 (KD-DTI).”
• An extensive review of tools for manual annotation of documents (2019, background): “co-references [96] and dependencies [7], as well as semantic relationships between entities, either binaries relations [8] or more complex biological events [95].”
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (2019, unknown): “DDI extraction 2013 corpus is a collection of 792 texts selected from the DrugBank database and other 233 Medline abstracts (Herrero-Zazo et al., 2013).”
• Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “These tasks, and the annotated DDI corpus [104] with >5000 DDIs, have contributed in a relevant way in the extraction and detection of DDIs from the literature [105].”
• Drug-Drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers (2017, methodology): “We use the DDI corpus of the 2013 DDIExtraction challenge [17] to train and test our model.”
• BioWordVec, improving biomedical word embeddings with subword information and MeSH (2019, methodology): “For the DDI extraction, we used the DDI 2013 corpus 35,36 , which is manually annotated and consists of five different DDI types, including Advice , Effect , Mechanism , Int and Negative .”
• In-BoXBART: Get Instructions into Biomedical Multi-Task Learning (2022, background): “, 2019), and (2) Drug-Drug Interaction (DDI) corpus from SemEval 2013 DDI Extraction challenge (Herrero-Zazo et al., 2013).”
• Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health (2023, methodology): “…prompt engineering method to evaluate ChatGPT’s performance on biomedical NER and RE in the BLURB benchmark datasets including BC5CDR-chemical [94], BC5CDR-disease [94], NCBI-disease [99], BC2GM [100], JNLPBA [101], ChemProt [102], DDI [103], and GAD [104] in a zero-shot or few-shot manner.”
• The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015, background): “In case of the DDI corpus, 700 documents (both PubMed abstracts and DrugBank records [19]) were annotated for drugs and relations between them [20], while the EDGAR corpus (103 PubMed abstracts about cancer) also contains annotations of drugs in addition to genes and cells [21].”
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “This instability is more matters for Healthcare than other general applications.”
• Extracting Drug-Drug Interactions with Attention CNNs (2017, methodology): “Still, the model outperformed several models such as Kim et al. (2015), Chowdhury and Lavelli (2013) and SCNN model even if we consider negative instance filtering.”
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (2022, methodology): “Relation Extraction GLRE [23], REBEL [24], seq2rel [25] KD-DTI [14], BC5CDR [13], DDI [15] Question Answering QA-Net [26], LUKE [27], BERT [2], PubMedBERT [9], BioELECTRA [28], LinkBERT [29] PubMedQA [16], BioASQ [30, 31] Document Classification BERT [2], BlueBERT [8], SciBERT [18], SPECTER [32],…”