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面向GWAS未覆盖基因组区的数据集成
  • ISSN号：1672-7207
  • 期刊名称：《中南大学学报：自然科学版》
  • 时间：0
  • 分类：TP399[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中南大学信息科学与工程学院,湖南长沙410083, [2]先进控制与智能自动化湖南省工程实验室,湖南长沙410083, [3]伊利诺伊大学芝加哥分校人类遗传学研究所,美国芝加哥60607
  • 相关基金：国家杰出青年基金资助项目（61125301）
作者： 裘嵘[1,2], 刘春宇[3], 吴敏[1,2]
关键词： 全基因组关联研究, 覆盖基因组区, 数据集成, GWAS, missing genomic regions, data integration
中文摘要：

针对面向整个全基因组关联研究（genome-wide association studies,GWAS）未覆盖基因组区的数据集成问题,提出基于自训练的半监督机器学习实现的语意映射技术应用于该研究领域的方法。研究结果表明：该方法能有效实现对整个GWAS未覆盖基因组区的自动的语意映射,精度达到94.2%,召回率达到97.5%,能有效降低对人类专家的依赖程度,实现对整个GWAS未覆盖基因组区数据的快捷有效集成。

英文摘要：

To solve the problem of data integration on the missing genomic regions associated with genome-wide association study （GWAS）, a method about the semantic mapping technique was put forward and investigated based on self-training half supervision machine learning. The results show that the method can effectively deal with the automatic semantic mapping for the missing genomic regions associated with whole GWAS with accuracy of 94.2% and recall rate of 97.5%, and effectively reduce the reliance on human experts. The method can quickly and effectively achieve the goal of data integration on the missing genomic regions of whole GWAS.

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期刊信息
  • 《中南大学学报：自然科学版》
  • 北大核心期刊（2011版）
  • 主管单位:教育部
  • 主办单位:中南大学
  • 主编：黄伯云
  • 地址：湖南长沙中南大学校本部
  • 邮编：410083
  • 邮箱：zngdxb@csu.edu.cn
  • 电话：0731-88879765
  • 国际标准刊号：ISSN：1672-7207
  • 国内统一刊号：ISSN：43-1426/N
  • 邮发代号:42-19
  • 获奖情况:
  • 首届全国优秀科技期刊评比一等奖,第二届全国优秀科技期刊评比一等奖,首届中国有色金属工业优秀科技期刊评比一等奖
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,荷兰文摘与引文数据库,美国工程索引,英国科学文摘数据库,中国中国科技核心期刊,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:20874