@inproceedings{sohrab-etal-2023-disease,
    title = "Disease Network Constructor: a Pathway Extraction and Visualization",
    author = "Sohrab, Mohammad Golam  and
      Duong, Khoa  and
      Topi{\'c}, Goran  and
      Ikeda, Masami  and
      Nagano, Nozomi  and
      Natsume-Kitatani, Yayoi  and
      Kuroda, Masakata  and
      Itoh, Mari  and
      Takamura, Hiroya",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.53",
    doi = "10.18653/v1/2023.acl-demo.53",
    pages = "549--557",
    abstract = "We present Disease Network Constructor (DNC), a system that extracts and visualizes a disease network, in which nodes are entities such as diseases, proteins, and genes, and edges represent regulation relation. We focused on the disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis (IPF). The front-end web-base user interface of DNC includes two-dimensional (2D) and 3D visualizations of the constructed disease network. The back-end system of DNC includes several natural language processing (NLP) techniques to process biomedical text including BERT-based tokenization on the basis of Bidirectional Encoder Representations from Transformers (BERT), flat and nested named entity recognition (NER), candidate generation and candidate ranking for entity linking (EL) or, relation extraction (RE), and event extraction (EE) tasks. We evaluated the end-to-end EL and end-to-end nested EE systems to determine the DNC{'}s back-endimplementation performance. To the best of our knowledge, this is the first attempt that addresses neural NER, EL, RE, and EE tasks in an end-to-end manner that constructs a path-way visualization from events, which we name Disease Network Constructor. The demonstration video can be accessed from \url{https://youtu.be/rFhWwAgcXE8}. We release an online system for end users and the source code is available at \url{https://github.com/aistairc/PRISM-APIs/}.",
}

We present Disease Network Constructor (DNC), a system that extracts and visualizes a disease network, in which nodes are entities such as diseases, proteins, and genes, and edges represent regulation relation. We focused on the disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis (IPF). The front-end web-base user interface of DNC includes two-dimensional (2D) and 3D visualizations of the constructed disease network. The back-end system of DNC includes several natural language processing (NLP) techniques to process biomedical text including BERT-based tokenization on the basis of Bidirectional Encoder Representations from Transformers (BERT), flat and nested named entity recognition (NER), candidate generation and candidate ranking for entity linking (EL) or, relation extraction (RE), and event extraction (EE) tasks. We evaluated the end-to-end EL and end-to-end nested EE systems to determine the DNC{'}s back-endimplementation performance. To the best of our knowledge, this is the first attempt that addresses neural NER, EL, RE, and EE tasks in an end-to-end manner that constructs a path-way visualization from events, which we name Disease Network Constructor. The demonstration video can be accessed from \url{https://youtu.be/rFhWwAgcXE8}. We release an online system for end users and the source code is available at \url{https://github.com/aistairc/PRISM-APIs/}.
Sohrab, Mohammad Golam Duong, Khoa Topi{\'c}, Goran Ikeda, Masami Nagano, Nozomi Natsume-Kitatani, Yayoi Kuroda, Masakata Itoh, Mari Takamura, Hiroya
Disease Network Constructor: a Pathway Extraction and Visualization