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Text-mined knowledgebase for AD pathology mechanism investigation

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AD-LitPathoNet

AD-LitPathoNet: A Resource of Pathology Network with Rich Literature Evidence for Alzheimer’s Disease

Pathology Network with Rich Literature Evidence for Alzheimer's disease (AD-LitPathoNet) is a text-mining based database, which is aim to facilitate the systematic knowledge curation and pathology mechanism investigation for AD.

AD-LitPathoNet keeps quarterly updates, and the latest data will updated to the database, which you can download directly without running any code.

System Requirements

The project code is mainly written in python 3.7 and depends on several APIs such as E-Utils, PubTator API, OGER, Phenotype. The complete pipeline dependencies can be found in requirement.txt.

The code was tested on centos 7 and can also be run on other linux kernel systems or windows. Most of the pipeline code supports multi-threading, which means more cpu cores to help update data faster, and a smooth network is needed to access these APIs.

AD-LitPathoNet relies in part on several other projects for data annotation, including the AGAC Biomedical Entity NER (https://github.com/YaoXinZhi/BERT-CRF-for-BioNLP-OST2019-AGAC-Task1) and the AGAC Thesis Role Identification ( https://github.com/YaoXinZhi/BERT-for-BioNLP-OST2019-AGAC-Task2).

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Text-mined knowledgebase for AD pathology mechanism investigation

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  • Python 87.8%
  • Jupyter Notebook 11.9%
  • Shell 0.3%