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Cancer survival classification using integrated data sets and intermediate information

ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第1期62卷 23-31页

作者： Kim, Shinuk Park, Taesung Kon, Mark Sangmyung Univ Coll Liberal Arts Cheonan 330729 Chungnam South Korea Seoul Natl Univ Dept Stat Seoul 151747 South Korea Boston Univ Dept Math & Stat Boston MA 02215 USA

Objective: Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. Methods: FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). Results: In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF;75%, SVM;75%, FSCOX_median;and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF;63.64%, SVM;72.73%, FSCOX_median;and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of m

关键词： Machine learning algorithm integration of data sets Intermediate information Survival time classification

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