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RNA family classification using the conditional random fields model


Paper Type 
Contributed Paper
Title 
RNA family classification using the conditional random fields model
Author 
Sitthichoke Subpaiboonkit[a], Chinae Thammarongtham[b] and Jeerayut Chaijaruwanich*[a,b,d]
Email 
jeerayut@science.cmu.ac.th
Abstract:

RNA  family  classification  is  one  of  the  necessary  tasks  needed  to  characterize  sequenced  genomes. RNA  families  are  defined  by  member  sequences  which  perform  the  same  function  in  different  species. Such  functions  have  a  strong  relationship  with  RNA  secondary  structures  but  not  the  primary  sequence. Thus  RNA  sequences  alone  are  not  sufficient  to  classify  RNA  families. Here, we  focus  on  computational  RNA  family  classification  by  exploring  primary  sequences  with  RNA  secondary  structures  as  the  selected  feature  to  classify  the  RNA  family  using  the  method  of conditional  random fields  (CRFs). This  model  treats  RNA  data  sets  with  optimal  F-score  prediction  between  98.77% - 99.32%  for different  RNA  families.

Start & End Page 
1 - 7
Received Date 
2011-06-21
Revised Date 
Accepted Date 
2011-10-06
Full Text 
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Keyword 
RNA family classification, Conditional random fields, bioinformatics, machine learning
Volume 
Vol.39 No.1 (JANUARY 2012)
DOI 
Citation 
Subpaiboonkit S., Thammarongtham C. and Chaijaruwanich[a,b,d] J., RNA family classification using the conditional random fields model, Chiang Mai J. Sci., 2012; 39(1): 1-7.
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Chiang Mai Journal of Science

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