A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Refining educational content through a closed-loop FLOW approach
ACM SIGCSE Bulletin
A Learning Method of Detecting Anomalous Pedestrian
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Predicting protein structural class from closed protein sequences
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Human motion recognition based on hidden Markov models
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Logic and model checking for hidden markov models
FORTE'05 Proceedings of the 25th IFIP WG 6.1 international conference on Formal Techniques for Networked and Distributed Systems
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The vast increase of data in biology has meant that many aspects of computational science have been drawn into the field. Two areas of crucial importance are large-scale data management and machine learning. The field between computational science and biology is varyingly described as "computational biology" or "bioinformatics." This paper reviews machine learning techniques based on the use of hidden Markov models (HMMs) for investigating biomolecular sequences. The approach is illustrated with brief descriptions of gene-prediction HMMs and protein family HMMs.