基于分子生物标记的中高危乳腺癌复发预测模型研究

项目名称： 基于分子生物标记的中高危乳腺癌复发预测模型研究

项目编号： No.61471147

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 李杰

作者单位： 哈尔滨工业大学

项目金额： 80万元

中文摘要： 癌症复发是指经过治疗肿瘤消失或无法探测到,经过一段时间后,同样的肿瘤再次出现。高精度癌症复发预测模型能够预测癌症复发风险，其在癌症基础研究、临床治疗和新药物研发中具有重要作用。中高危乳腺癌存活率低，侵袭性强，当前的中高危乳腺癌复发预测模型预测精度低，稳定性差，不能满足临床的急迫需求。乳腺癌复发机制复杂，复发相关的高通量分子数据储存在多个公共数据库和不同实验室数据库里，复发相关的信息分布在各种疾病知识库里，发展高精度中高危乳腺癌复发预测模型需要集成这些信息和数据,但是,集成这些信息和数据面临着多种挑战。本项目拟集成乳腺癌相关知识和高通量生物数据，设计分子生物标记识别和选择关键算法，在此基础上发展高精度中高危乳腺癌复发预测模型。

中文关键词： 致病基因挖掘；多组学数据融合；多组学数据挖掘

英文摘要： Characters)：A cancer recurrence is defined as a return of cancer after treatment and after a period of time during which the tumor disappears or cannot be detected.High accuracy cancer recurrence prediction model can predict the risk of cancer recurrence, which plays an important role in basic cancer research, clinical treatment, new drug development. Intermediate and high risk breast cancer has low survival rate and high aggressive. The predictive accuracy of existing recurrence prediction models for intermediate and high risk breast cancer is still low， their stability is poor and can not meet the urgent needs of clinic. Mechanism of breast cancer recurrence is complex,recurrence-related high-throughput molecular data is stored in a number of public databases and different laboratory databases, recurrence-related information is distributed in a number of disease knowledge databases. It needs to integrate a wide range of information and data to develop high accuracy recurrence prediction model for intermediate and high risk breast cancer. However,effective integration of these knowledge and data from multiple sources is a challenging task.The project plans to integrate breast cancer-related knowledge and high throughput biology data,design key algorithms of identifying and selecting molecular biomarkers, and develop breast cancer recurrence prediction models with high accuracy.

英文关键词： disease gene mining;multi-omics data fusion;multi-omics data mining