On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth 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
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Ranking and selection of features for improved prediction of nucleosome occupancy and modification
MCBC'08 Proceedings of the 9th WSEAS International Conference on Mathematics & Computers In Biology & Chemistry
| Hi-index | 0.00 |
Eukaryotic genomes are packaged by the wrapping of DNA around histone octamers to form nucleosomes. Nucleosome occupancies together with their acetylation and methylation are important modification factors on all nuclear processes involving DNA. There have been recently many studies of mapping these modifications in DNA sequences and of relationship between them and various genetic activities, such as transcription, DNA repair, and DNA remodeling. However, most of these studies are experimental approaches. In this paper, we introduce a computational approach to both predicting and analyzing nucleosome occupancy, acetylation, and methylation areas in DNA sequences. Our method employs conditional random fields (CRFs) to discriminate between DNA areas with high and low relative occupancy, acetylation, or methylation; and rank features of DNA sequences based on their weight in the CRFs model trained from the datasets of these DNA modifications. The results from our method on the yeast genome reveal genetic area preferences of nucleosome occupancy, acetylation, and methylation are consistent with previous studies.