BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets.

[1]  Yutaka Sasaki,et al.  Integrating heterogeneous knowledge graphs into drug–drug interaction extraction from the literature , 2022, Bioinform..

[2]  Rui Zhang,et al.  Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature , 2022, J. Biomed. Informatics.

[3]  Yoav Goldberg,et al.  A Dataset for N-ary Relation Extraction of Drug Combinations , 2022, NAACL.

[4]  C. Arighi,et al.  BioRED: a rich biomedical relation extraction dataset , 2022, Briefings Bioinform..

[5]  Ruizhang Huang,et al.  Protein-protein interaction relation extraction based on multigranularity semantic fusion , 2021, J. Biomed. Informatics.

[6]  Juha Iso-Sipilä,et al.  Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text , 2020, LOUHI.

[7]  Jing Huang,et al.  Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling , 2020, AAAI.

[8]  Thomas C. Wiegers,et al.  Comparative Toxicogenomics Database (CTD): update 2021 , 2020, Nucleic Acids Res..

[9]  Ying Liu,et al.  Deep learning for drug-drug interaction extraction from the literature: a review , 2020, Briefings Bioinform..

[10]  Sriparna Saha,et al.  Relation Extraction From Biomedical and Clinical Text: Unified Multitask Learning Framework , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  Valeriia Haberland,et al.  EpiGraphDB: a database and data mining platform for health data science , 2020, bioRxiv.

[12]  Jianfeng Gao,et al.  Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing , 2020, ACM Trans. Comput. Heal..

[13]  R. D. Boyce,et al.  Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance , 2020, medRxiv.

[14]  Steven Bethard,et al.  A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction , 2020, BIONLP.

[15]  Qingyu Chen,et al.  An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining , 2020, BIONLP.

[16]  Yijia Zhang,et al.  Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation , 2020, JMIR medical informatics.

[17]  F. Sanz,et al.  The DisGeNET knowledge platform for disease genomics: 2019 update , 2019, Nucleic Acids Res..

[18]  Robert Leaman,et al.  PubTator central: automated concept annotation for biomedical full text articles , 2019, Nucleic Acids Res..

[19]  Ye Wu,et al.  RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature , 2019, RECOMB.

[20]  Aidong Zhang,et al.  A survey on literature based discovery approaches in biomedical domain , 2019, J. Biomed. Informatics.

[21]  Qingyu Chen,et al.  Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine , 2019, Database J. Biol. Databases Curation.

[22]  Fei Wang,et al.  A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization , 2018, AAAI.

[23]  Cathy H. Wu,et al.  Pattern Discovery for Wide-Window Open Information Extraction in Biomedical Literature , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[24]  Tejas Shah,et al.  LION LBD: a literature-based discovery system for cancer biology , 2018, Bioinform..

[25]  Yifan Peng,et al.  Extracting chemical–protein relations with ensembles of SVM and deep learning models , 2018, Database J. Biol. Databases Curation.

[26]  Francisco M. Couto,et al.  Extracting microRNA-gene relations from biomedical literature using distant supervision , 2017, PloS one.

[27]  Yifan Peng,et al.  Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task , 2016, Database J. Biol. Databases Curation.

[28]  Rong Xu,et al.  Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature , 2014, J. Biomed. Informatics.

[29]  Paloma Martínez,et al.  The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions , 2013, J. Biomed. Informatics.

[30]  Juliane Fluck,et al.  Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports , 2012, J. Biomed. Informatics.

[31]  Olivier Bodenreider,et al.  Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature , 2011, Bioinform..

[32]  Jari Björne,et al.  All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning , 2008, BMC Bioinformatics.

[33]  Ralf Zimmer,et al.  RelEx - Relation extraction using dependency parse trees , 2007, Bioinform..

[34]  Rohit J. Kate,et al.  Comparative experiments on learning information extractors for proteins and their interactions , 2005, Artif. Intell. Medicine.

[35]  T. Jenssen,et al.  A literature network of human genes for high-throughput analysis of gene expression , 2001, Nature Genetics.

[36]  C E Lipscomb,et al.  Medical Subject Headings (MeSH). , 2000, Bulletin of the Medical Library Association.

[37]  Vijay K. Shanker,et al.  BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA , 2021, BIONLP.

[38]  OUP accepted manuscript , 2021, Bioinformatics.

[39]  Xiaojie Yuan,et al.  An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization , 2021, ACL.

[40]  Malaikannan Sankarasubbu,et al.  BioELECTRA:Pretrained Biomedical text Encoder using Discriminators , 2021, BIONLP.

[41]  Ming Li,et al.  Discovering patterns to extract protein-protein interactions from full texts. , 2004, Bioinformatics.