constants.py

CITATIONS = {
"blurb": """\
            @article{2022,
              title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
              volume={3},
              ISSN={2637-8051},
              url={http://dx.doi.org/10.1145/3458754},
              DOI={10.1145/3458754},
              number={1},
              journal={ACM Transactions on Computing for Healthcare},
              publisher={Association for Computing Machinery (ACM)},
              author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
              year={2022},
              month={Jan},
              pages={1–23} 
              }
            """,
  "BC5CDR-chem-IOB":  """@article{article,
          author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
          year = {2016},
          month = {05},
          pages = {baw068},
          title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
          volume = {2016},
          journal = {Database},
          doi = {10.1093/database/baw068}
          }""",
  "BC5CDR-disease-IOB":"""@article{article,
          author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
          year = {2016},
          month = {05},
          pages = {baw068},
          title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
          volume = {2016},
          journal = {Database},
          doi = {10.1093/database/baw068}
          }""",
  "BC2GM-IOB":"""@article{article,
          author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
          year = {2008},
          month = {09},
          pages = {S2},
          title = {Overview of BioCreative II gene mention recognition},
          volume = {9 Suppl 2},
          journal = {Genome biology},
          doi = {10.1186/gb-2008-9-s2-s2}
          }""",
  "NCBI-disease-IOB":"",
  "JNLPBA":"",

}

DESCRIPTIONS = {
  "blurb" : """BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
              is a comprehensive benchmark for biomedical NLP, with 13 biomedical NLP datasets in 6 
              tasks (NER, PICO, Relation Extraction, Sentence similarity, document classification, question answering). 
              Our aim is to facilitate investigations of biomedical natural language processing 
              with a specific focus on language model pretraining and to help accelerate progress in universal Biomedical 
              NLP applications. The table below compares the datasets comprising BLURB versus the various datasets used in 
              previous Biomedical and Clinical BERT language models.""",
  "BC5CDR-chem-IOB":"""The corpus consists of three separate sets of 
                    articles with diseases, chemicals and their relations annotated. 
                    The training (500 articles) and development (500 articles) sets 
                    were released to task participants in advance to support text-mining 
                    method development. The test set (500 articles) was used for final 
                    system performance evaluation.""",
  "BC5CDR-disease-IOB":"""The corpus consists of three separate sets of 
                    articles with diseases, chemicals and their relations annotated. 
                    The training (500 articles) and development (500 articles) sets 
                    were released to task participants in advance to support text-mining 
                    method development. The test set (500 articles) was used for final 
                    system performance evaluation.""",
  "BC2GM-IOB":"""The BioCreative II Gene Mention task.
                    The training corpus for the current task consists mainly of 
                    the training and testing corpora (text collections) from the 
                    BCI task, and the testing corpus for the current task 
                    consists of an additional 5,000 sentences that were held 
                    'in reserve' from the previous task.
                    In the current corpus, tokenization is not provided; 
                    instead participants are asked to identify a gene mention 
                    in a sentence by giving its start and end characters. 
                    As before, the training set consists of a set of sentences, 
                    and for each sentence a set of gene mentions 
                    (GENE annotations).
                    """,
  "NCBI-disease-IOB":"",
  "JNLPBA":"",
}

HOMEPAGES = {
  "blurb": "https://microsoft.github.io/BLURB/index.html",
  "BC5CDR-chem-IOB":"https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus",
  "BC5CDR-disease-IOB":"https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus",
  "BC2GM-IOB": "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/"
  "NCBI-disease-IOB":"",
  "JNLPBA":"",

}


DATA_URL = {
  "blurb": None,
  "BC5CDR-chem-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
  "BC5CDR-disease-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
  "BC2GM-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
  "NCBI-disease-IOB":"",
  "JNLPBA":"",
}