Regulation of nuclear SMAD23 signaling (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.本数据集为原始美国国家癌症研究所通路相互作用数据库（National Cancer Institute Pathway Interaction Database，PID）的更新版本。 研究团队采用该原始网络所包含的实体（基因、蛋白质、化学小分子等）列表，对INDRA数据库（INDRA）进行查询，从而获取实体间的高质量关联关系。INDRA数据库整合了来自多个高质量文本挖掘引擎、通路数据库以及小分子资源的多源信息。经由INDRA衍生得到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，其中包含具体的支撑文本段落。 尽管本数据集仅收录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，例如上调与下调关系的反转，或是关联边的方向标注错误（即本应标注A激活B，却误标注为B激活A）。尽管存在此类潜在误差，文本挖掘工具对实体的识别效果仍极为出色，且支撑某一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标注的关联边。 红色：同时由INDRA与PID标注的关联边。 黄色：选定的元素。