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中文摘要：
卵巢癌是目前死亡率最高的妇科疾病之一,而如果得到早期诊断和治疗,卵巢癌患者的存活率可达90%。针对卵巢癌早期诊断问题,基于卵巢癌磷脂质类数据,提出了一种结合缠绕法和过滤法、按照诊断类别相关度挑选特征,然后依据特征标志物的分类率稳定度高低,提取用于诊断早期卵巢癌的特征子集的策略。该方法克服了分类率监督方法忽略生物相关性、依赖分类器易产生过拟合的不足,同时保持了较高的分类率。实验表明,该方法挑选的特征标志物包含更多的分类信息,其分类正确率达到88.9%,且比经典的分类率监督方法和差异表达方法在稳定性能上存在优势。此外,提出的新的标幺化方法去掉了批次差异,获得更好的分类效果,且所选的特征标志物得到生物学关联意义上的支持,具有较高的可信度和实用性。
英文摘要：
Ovarian Carcinoma（OvCa）is the most lethal type of gynecological cancer.However it is shown that about 90% patients could be saved if they were diagnosed and treated in the early stage.In this study,we propose a new strategy in which the biomarkers are identified in terms of their relevance to the clinical outcome and stability.Comparative study and statistical analysis show that the proposed method outperforms SVM-RFE and T-test methods in stability,which are the typical supervised classification and differential expression detection based feature selection methods,and achieves satisfying classification result（88.9%）as well.The reliability of the identified biomarkers is also biologically validated and supported by relevant biological research.
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