Direct p53 effectors (v2.0)

This is the updated version of an original NCI Pathway Interaction Database (PID) network.
The list of entities (genes, proteins, chemicals, etc) from the original network was used to query the INDRA database, retrieving high-quality relationships between entities. INDRA is a database that integrates information from multiple high-quality text mining engines, pathway databases, and small molecule resources. The INDRA-derived relationships are up-to-date and have links to detailed summaries of supporting literature evidence, including the specific supporting text.
While we have included only high-confidence relationships, text-mining complicated sentences can produce errors such as the reversal of up-regulation vs. down-regulation or the direction of an edge (i.e. B activates A instead of A activates B). Nevertheless, the entity recognition by the text miners is excellent and the text supporting a relationship almost always describes a genuine relationship between the entities.
Legend:
BLUE: edges annotated by INDRA only.
RED: edges annotated both by INDRA and PID.
YELLOW: selected element.

本数据集为原始国家癌症研究所通路相互作用数据库（NCI Pathway Interaction Database，PID）网络的更新版本。研究人员以原始网络中的实体（基因、蛋白质、小分子化合物等）列表为查询对象，检索INDRA数据库，以获取实体间的高质量关联关系。INDRA数据库是一个整合多源高质量文本挖掘引擎、通路数据库及小分子资源信息的综合数据库。由INDRA提取的关联关系均为最新版本，且附带支持性文献证据的详细摘要及相关链接，其中包含支撑该关联的具体文本片段。
尽管本数据集仅保留了高置信度的关联关系，但文本挖掘技术在处理复杂语句时仍可能产生误差，例如将上调与下调关系颠倒，或是搞错网络边的作用方向（如标注为B激活A，实际应为A激活B）。尽管如此，文本挖掘工具对实体的识别效果优异，且支撑关联关系的文本几乎总能准确反映实体间的真实关联。
图例说明：
蓝色：仅由INDRA标注的网络边。
红色：同时由INDRA与PID标注的网络边。
黄色：选中的元素。