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RNA Secondary Structure Prediction Using Neural Machine Translation Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_projects/pv63g4248

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  • RNA secondary structure prediction maps a RNA sequence to its secondary structure (set of AU, CG, and GU pairs). It is an important problem in computational biology be-cause such structures reveals crucial information about the RNAs function, which is useful in many applications ranging from noncoding RNA detection to folding dynamics simulation. Traditionally, RNA structure prediction is often accomplished computationally by the cubic-time CKY parsing algorithm borrowed from computational linguistics, with the energy parameters either estimated physically or learned from data. With the advent of deep learning, we propose a brand-new way of looking at this problem, and cast it as a machine translation problem where the RNA sequence is the source language and the dot-parenthesis structure is the target language. Using a state-of-the-art open source neural machine translation package, we are able to build an RNA structure predictor without any hand-designed features.
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  • description.provenance : Submitted by Luyao Zhang (zhangluy@oregonstate.edu) on 2016-09-13T04:10:14ZNo. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)ZhangLuyao2016.pdf: 1589424 bytes, checksum: 9ee994df0c8c5496d9028e675abc5b29 (MD5)
  • description.provenance : Made available in DSpace on 2016-09-16T22:15:34Z (GMT). No. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)ZhangLuyao2016.pdf: 1589424 bytes, checksum: 9ee994df0c8c5496d9028e675abc5b29 (MD5) Previous issue date: 2016-08-30
  • description.provenance : Approved for entry into archive by Steven Van Tuyl(steve.vantuyl@oregonstate.edu) on 2016-09-16T22:15:33Z (GMT) No. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)ZhangLuyao2016.pdf: 1589424 bytes, checksum: 9ee994df0c8c5496d9028e675abc5b29 (MD5)

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