On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Efficient learning of context-free grammars from positive structural examples
Information and Computation
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Bioinformatics
RnaPredict—An Evolutionary Algorithm for RNA Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Non-coding RNAs ncRNAs have important biological functions in living cells dependent on their conserved secondary structures. Here, we focus on computational RNA secondary structure prediction by exploring primary sequences and complementary base pair interactions using the Conditional Random Fields CRFs model, which treats RNA prediction as a sequence labelling problem. Proposing suitable feature extraction from known RNA secondary structures, we developed a feature extraction based on natural RNA's loop and stem characteristics. Our CRFs models can predict the secondary structures of the test RNAs with optimal F-score prediction between 56.61 and 98.20% for different RNA families.