HMM-ModE - Improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying e

Background: Profile Hidden Markov Models (HMM) are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives). Ten-fold cross validation is used to ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Prashant K. Srivastava, Dhwani K. Desai, Soumyadeep Nandi, Andrew M. Lynn
• Keywords: BMCBI 2007, Negative Training Sequences, Sequences, Specific Signals