Paper Info
Title
A hidden Markov model for gene function prediction from sequential expression data
Abstract
Hidden Markov models (HMMs) have demonstrated great successes in modeling noisy sequential data sets in the area of speech recognition and protein sequence profiling. Results from association test showed significant Markov dependency in time-series gene expression data, and therefore HMMs would be especially appropriate for modeling gene expressions. In this project, we developed a gene function prediction tool based on profile HMMs. Each function class is associated with a distinct HMM whose parameters are trained using yeast time-series gene expression data. The function annotations of the HMM training set were obtained from Munich Information Centre for Protein Sequences (MIPS) data base. We designed several structural variants of HMMs (single, double-split) and tested each of them on forty function classes each of which includes more than one hundred instances. The highest prediction sensitivity we achieved is 51% by using double-split HMM with 3-fold cross-validation.
Year
DOI
Venue
2004
10.1109/CSB.2004.1332541
CSB
Keywords
Field
DocType
function class,gene function prediction,genetics,forty function class,sequential expression data,time-series gene expression data,gene expression,proteins,function annotations,profile hmms,biology computing,function annotation,molecular biophysics,yeast time-series gene expression data,hidden markov model,data base,prediction theory,gene function prediction tool,munich information centre for protein sequences data base,hidden markov models,yeast time-series gene expression,time series,noisy sequential data set,cross validation,speech recognition,protein sequence
Training set,Sequential data,Gene,Expression (mathematics),Protein sequencing,Profiling (computer programming),Computer science,Markov chain,Artificial intelligence,Bioinformatics,Hidden Markov model,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-2194-0
6
0.47
References 
Authors
0
2
Name
Order
Citations
PageRank
Xutao Deng1868.22
Hesham Ali2295.18