A Multi-Label Classification Framework to Predict Disease Associations of Long Non-coding RNAs (lncRNAs)

  • Ashis Kumer Biswas
  • Baoju Zhang
  • Xiaoyong Wu
  • Jean X. Gao
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 322)


In this paper, the automated detection of tissue specific disease association of long non-coding RNAs (lncRNAs) is modeled as a multi-label classification task, where a single lncRNA transcript may be associated with several diseases in a tissue specific manner. Four algorithms are evaluated and compared in this task. Furthermore, in this article we put emphasis on the fact that secondary structure and the composition features of the lncRNAs dictate their functions that led us to develop a new multi-label feature extraction scheme. Experiments are conducted on a set of 7,566 lncRNA transcripts with 22 tissue labels, and the results provide interesting insights into the quality of the discussed algorithms and the features.


Long non-coding RNA Multi-label classification Association 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ashis Kumer Biswas
    • 1
  • Baoju Zhang
    • 2
  • Xiaoyong Wu
    • 2
  • Jean X. Gao
    • 1
  1. 1.Department of Computer Science and EngineeringThe University of Texas at ArlingtonArlingtonUSA
  2. 2.School of Communications and Electronic InformationTianjin Normal UniversityTianjinChina

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