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- W2384610928 abstract "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." @default.
- W2384610928 created "2016-06-24" @default.
- W2384610928 creator A5076042378 @default.
- W2384610928 date "2010-01-01" @default.
- W2384610928 modified "2023-09-25" @default.
- W2384610928 title "Lysophospholipid Data Analysis and Biomarker Selection for Early Ovarian Cancer Detection" @default.
- W2384610928 hasPublicationYear "2010" @default.
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