阿尔茨海默病基因-疾病关联的知识挖掘

目的/意义] 对阿尔茨海默病（AD）进行基因-疾病关联挖掘,以捕捉潜力研究方向。方法/过程] 基于LBD理论构建开放式知识发现架构,结合MeSH词表、DisGeNET等医学术语、组学数据对PubMed中AD文献进行知识挖掘,采用关联规则与算法排序等方法对部分基因重合的强关联主题共现疾病和优先候选基因进行筛选,结合时间切片和其他LBD工具对比加以验证。结果/结论] 对88 334篇AD文献进行基因-疾病识别,并与2 120种AD基因进行匹配;以XYZ分析视角对识别出的992种主题共现疾病及11 899种候选基因进行关联排序;精炼10种强关联疾病与25种优选候选基因,结合文献报道加以论述。通过LBD挖掘目标疾病-共现疾病-基因之间潜在关联,可快速捕捉潜力研究方向,缩小基因测序范围,为新研究假设的生成提供重要指导依据。

Knowledge Mining of Alzheimer's Disease Gene-Disease Associations

Purpose/significance] To explore the gene-disease association of Alzheimer's disease (AD) in order to capture the potential research directions.Method/process] An open knowledge discovery framework was constructed based on LBD theory. Combined with MeSH thesaurus, DisGeNET and other medical terms and group data, knowledge mining was carried out in AD literatures in PubMed. Association rules and algorithm sorting were used to screen strongly associated MeSH terms co-occurrence diseases and priority candidate genes for partial gene coincidence, results of time slicing and comparison with other LBD tools were used to verify them.Result/conclusion] 88 334 AD literatures were identified and matched with 2 120 AD genes, 11 899 candidate genes and 992 comorbidity genes were identified according to XYZ analysis, 10 strongly associated co-occurrence diseases and 25 preferred candidate genes were refined and discussed in combination with literature reports. Mining the potential associations between target disease, co-occurrence diseases and genes by LBD can quickly capture the potential research directions, narrow the scopes of gene sequencing, and provide important guidance for the generations of new research hypotheses.