eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines.

Journal: Hereditas
Published Date: Jan 1, 2016

Abstract

BACKGROUND: Enhancers are tissue specific distal regulation elements, playing vital roles in gene regulation and expression. The prediction and identification of enhancers are important but challenging issues for bioinformatics studies. Existing computational methods, mostly single classifiers, can only predict the transcriptional coactivator EP300 based enhancers and show low generalization performance.

Authors

  • Fang Huang
  • Jiawei Shen
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Qingli Guo
    Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) and the Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030 People's Republic of China.
  • Yongyong Shi
    Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) and the Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030 People's Republic of China ; Shanghai Changning Mental Health Center, Shanghai, 200042 People's Republic of China ; Department of Psychiatry, The First Teaching Hospital of Xinjiang Medical University, Urumqi, 830054 People's Republic of China ; The Bio-X Little White Building, Shanghai Jiao Tong University, No.55 Guang Yuan Xi Road, Shanghai, 200030 China.

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