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Paper   IPM / Biological Sciences / 14386
School of Biological Sciences
  Title:   rpiCOOL: A tool for In Silico RNA protein interaction detection using random forest
1.  Mohammad Akbaripour-Elahabad
2.  Javad Zahiri
  Status:   Published
  Journal: Journal of Theoretical Biology
  Vol.:  402
  Year:  2016
  Pages:   1-8
  Supported by:  IPM
Understanding the principle of RNA�??protein interactions (RPIs) is of critical importance to provide insights into post-transcriptional gene regulation and is useful to guide studies about many complex diseases. The limitations and difficulties associated with experimental determination of RPIs, call an urgent need to computational methods for RPI prediction. In this paper, we proposed a machine learning method to detect RNA�??protein interactions based on sequence information. We used motif information and repetitive patterns, which have been extracted from experimentally validated RNA�??protein interactions, in combination with sequence composition as descriptors to build a model to RPI prediction via a random forest classifier. About 20

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