title: Cancer survival classification using integrated data sets and intermediate information.
abstract: 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 miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles.
  
  
  CONCLUSIONS
  Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses.
authors.affiliation: College of Liberal Arts, Sangmyung University, 31 Sangmyungdae-gil, Cheonan, Chungnam 330-729, Republic of Korea; Department of Statistics, Seoul National University, 1 Gwankak-ro, Seoul 151-747, Republic of Korea; Department of Mathematics and Statistics, Boston University, 111 Cummington Mall, Boston, MA 02215, USA. Electronic address: kshinuk@smu.ac.kr.
authors.forename: Shinuk
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, 1 Gwankak-ro, Seoul 151-747, Republic of Korea. Electronic address: tspark@snu.ac.kr.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
authors.affiliation: Department of Mathematics and Statistics, Boston University, 111 Cummington Mall, Boston, MA 02215, USA. Electronic address: mkon@bu.edu.
authors.forename: Mark
authors.initials: M
authors.lastname: Kon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality*; D066264:Datasets as Topic; D005260:Female; D005909:Glioblastoma / Q000235:genetics / Q000378:metabolism / Q000401:mortality*; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality*; D016016:Proportional Hazards Models; D012333:RNA, Messenger / Q000378:metabolism; D012680:Sensitivity and Specificity; D015996:Survival Rate
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger
keywords: Integration of data sets; Intermediate information; Machine learning algorithm; Survival time classification