Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer
作者: Mehmet Eren AhsenTodd P. BorenNitin K. SinghBurook MisganawJayanthi S. LeaDavid S. MillerMichael A. WhiteMathukumalli Vidyasagar
作者单位: IBM Research, Yorktown Heights, NY, USA;;The University of Tennessee, Knoxville, TN, USA;;Apple Inc., Austin, TX, USA;;Harvard University, Cambridge, MA,USA;;University of Texas, Southwestern Medical Center, Dallas, TX, USA;;University of Texas, Southwestern Medical Center, Dallas, TX, USA;;University of Texas, Southwestern Medical Center, Dallas, TX, USA;;University of Texas at Dallas, Richardson, TX, USA
论文集英文名称: Bioinformatics, Computational Biology, and Health Informatics
来源数据库: Association for Computing Machinery
DOI: 10.1145/2975167.2985667
关键词: Effect sizeMeta-analysisPathway
原始语种摘要: Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. We introduce a new feature selection algorithm, lone star, for applications where the number of samples is far smaller than the number of measured features per sample. We applied lone star to develop...
全文获取路径: ACM  (合作)
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关键词翻译
关键词翻译
  • Selection 分选
  • feature 结构元件
  • signature 签名
  • classifier 分级机
  • correctly 准确地
  • endometrial 子宫内膜的
  • significant 有效的
  • classify 分类
  • robust 牢固的
  • improvement 改良