CIViCmine

This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.本说明针对CIViCmine项目的输出文件。此类文件可直接由CIViCmine查看器（viewer）加载。若您希望独立部署该查看器，其代码可在CIViCmine的GitHub仓库中获取。所有文件均为带表头的制表符分隔文本文件，无注释行，且未使用引号包裹内容。 若仅需获取癌症生物标志物列表，推荐使用<civicmine_collated.tsv>；若需查看支撑性文献句子，请使用<civicmine_sentences.tsv>，二者可通过<matching_id>列完成关联。若希望进一步深挖数据且可接受较高的假阳性率，请查看<civicmine_unfiltered.tsv>。 <civicmine_collated.tsv>：该文件收录带有支撑引用计数的癌症生物标志物，包含标准化后的癌症与基因名称，以及人类基因命名委员会（Human Genome Organization，简称HUGO）、Entrez Gene及疾病本体论（Disease Ontology）的对应标识。 <civicmine_sentences.tsv>：该文件收录整合文件中癌症生物标志物的支撑性文献句子。每一行对应某一癌症生物标志物的单条支撑句子，包含来源出版物信息（如期刊、发表日期等）、原文句子及提取得到的癌症生物标志物相关信息。 <civicmine_unfiltered.tsv>：该文件为applyModelsToSentences.py脚本针对PubMed、PubMed Central开放获取库及PubMed Central作者手稿合集全量数据运行后的原始输出结果，收录所有预测得分高于0.5的关联关系，因此可能包含大量假阳性条目。每一行均包含出版物信息（如期刊、发表日期等）、原文句子及提取得到的具体癌症生物标志物（附带HUGO、Entrez Gene及疾病本体论标识）。该文件经进一步处理后生成了前述两个文件。