• 主题
高级搜索  >
历史搜索 清空历史
首页> 中文期刊> 《癌症中的人工智能（英文）》 >Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

         
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BACKGROUND In their everyday life,clinicians face an overabundance of biological indicators potentially helpful during a disease therapy.In this context,to be able to reliably identify a reduced number of those markers showing the ability of optimising the classification of treatment outcomes becomes a factor of vital importance to medical prognosis.In this work,we focus our interest in inflammatory bowel disease(IBD),a long-life threaten with a continuous increasing prevalence worldwide.In particular,IBD can be described as a set of autoimmune conditions affecting the gastrointestinal tract whose two main types are Crohn’s disease and ulcerative colitis.AIM To identify the minimal signature of microRNA(miRNA)associated with colorectal cancer(CRC)in patients with one chronic IBD.METHODS We provide a framework of well-established statistical and computational learning methods wisely adapted to reconstructing a CRC network leveraged to stratify these patients.RESULTS Our strategy resulted in an adjusted signature of 5 miRNAs out of approximately 2600 in Crohn’s Disease(resp.8 in Ulcerative Colitis)with a percentage of success in patient classification of 82%(resp.81%).CONCLUSION Importantly,these two signatures optimally balance the proportion between the number of significant miRNAs and their percentage of success in patients’stratification.

著录项

相似文献

  • 中文文献
  • 外文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号