Abstract
Existing data mining tools can only achieve about 40% precision in function prediction of unannotated genes. We developed a gene function prediction tool based on profile Hidden Markov Models (HMMs). Each function class was modelled using a distinct HMM whose parameters were trained using yeast time-series gene expression profiles. Two structural variants of HMMs were designed and tested, each of them on 40 function classes. The highest overall prediction precision achieved was 67% using double-split HMM with leave-one-out cross-validation. We also attempted to generalise HMMs to dynamic Bayesian networks for gene function prediction using heterogeneous data sets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Bioinformatics Research and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.