A random forest classifier predicts recurrence risk in patients with ovarian cancer
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چکیده
منابع مشابه
Evaluation of risk factors of recurrence of hodgkin\'s lymphoma using random survival forest and comparison with cox regression model
Background: In many studies, Cox regression was used to assess the important factors that affect the survival of cancer patients based on demographic and clinical variables. The aim of this study was to determine the factors affecting the survival of patients with Hodgkin's lymphoma using the random survival forest (RSF) method and compare it with the Cox model. Methods: In this retrospective ...
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The original Random Forest derives the final result with respect to the number of leaf nodes voted for the corresponding class. Each leaf node is treated equally and the class with the most number of votes wins. Certain leaf nodes in the topology have better classification accuracies and others often lead to a wrong decision. Also the performance of the forest for different classes differs due ...
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We previously identified 34 genes of interest (GOI) in 2006 to aid the oncologists to determine whether post-mastectomy radiotherapy (PMRT) is indicated for certain patients with breast cancer. At this time, an independent cohort of 135 patients having DNA microarray study available from the primary tumor tissue samples was chosen. Inclusion criteria were 1) mastectomy as the first treatment, 2...
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BACKGROUND The consequences of defective homologous recombination (HR) are not understood in sporadic ovarian cancer, nor have the potential role of HR proteins other than BRCA1 and BRCA2 been clearly defined. However, it is clear that defects in HR and other DNA repair pathways are important to the effectiveness of current therapies. We hypothesize that a subset of sporadic ovarian carcinomas ...
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ژورنال
عنوان ژورنال: Molecular Medicine Reports
سال: 2018
ISSN: 1791-2997,1791-3004
DOI: 10.3892/mmr.2018.9300