• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Bioinformatics and Computational Biology >The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning
【24h】

The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning

机译：基于改进多核学习的乳腺癌分级预测与通路分析方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Breast cancer histologic grade represents the morphological assessment of the tumor's malignancy and aggressiveness, which is vital in clinically planning treatment and estimating prognosis for patients. Therefore, the prediction of breast cancer grade can markedly elevate the detection of early breast cancer and eff ciently guide its treatment. With the advent of highthroughput profiling technology, a large number of data of di r erent types are rapidly generated, and each data provides its unique biological insight. Although many researches focused on cancer grade prediction, hardly most of them attempted to integrate multiple data types, by which we cannot only improve and boost results obtained from learning method, but also have a good understanding or explanation of biological issues. In this paper, we take advantage of a sophisticated supervised learning method called multiple kernel learning (MKL) to design a breast cancer grading predictor fusing heterogeneous data for classification of breast cancer histopathology. Furthermore, we modify our model by involving biological pathway information. The new model can evaluate the significance of various pathways in which di r erential expression genes fall between di r erent breast cancer grades. The merits of the novel model are lucubration in bridging between omics data and various phenotypes of breast cancer grades, and providing an auxiliary method integrating omics data of cancer mechanism research.
机译：乳腺癌组织学分级代表了对肿瘤恶性和侵袭性的形态学评估，这对于临床规划治疗和估计患者的预后至关重要。因此，乳腺癌分级的预测可以显著提高早期乳腺癌的检出率，并有效地指导其治疗。随着高通量分析技术的出现，大量不同类型的数据被快速生成，每个数据都提供了其独特的生物学见解。尽管许多研究都集中在癌症分级预测上，但几乎没有大多数研究试图整合多种数据类型，通过这些研究，我们不仅可以改进和促进从学习方法中获得的结果，而且可以很好地理解或解释生物学问题。在本文中，我们利用一种称为多核学习 （MKL） 的复杂监督学习方法来设计一种融合异质数据的乳腺癌分级预测因子，用于乳腺癌组织病理学分类。此外，我们通过涉及生物途径信息来修改我们的模型。新模型可以评估不同表达基因介于不同乳腺癌等级之间的各种途径的重要性。该模型的优点是能够将组学数据与乳腺癌分级的各种表型联系起来，为癌症机制研究的组学数据提供整合的辅助方法。

著录项

相似文献

  • 外文文献
  • 中文文献
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