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Related Experiment Video

Updated: Jul 3, 2026

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

A Hidden Markov Model approach to predicting yeast gene function from sequential gene expression data.

Xutao Deng¹, Huimin Geng, Hesham H Ali

¹Cedars-Sinai Medical Center, School of Medicine, University of California, Los Angeles CA 90048, USA. dengx@cshs.org

International Journal of Bioinformatics Research and Applications

|July 22, 2008

Summary

This summary is machine-generated.

We developed a novel gene function prediction tool using profile Hidden Markov Models (HMMs), improving prediction precision from 40% to 67%. This advancement aids in understanding unannotated gene functions more accurately.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate gene function prediction is crucial for understanding biological systems.
Current data mining tools achieve limited precision (around 40%) for unannotated genes.

Purpose of the Study:

To develop an improved gene function prediction tool.
To enhance the precision of predicting functions for unannotated genes.

Main Methods:

Utilized profile Hidden Markov Models (HMMs) to model gene function classes.
Trained HMM parameters using yeast time-series gene expression profiles.
Tested two structural variants of HMMs on 40 function classes.

Main Results:

Achieved a highest overall prediction precision of 67% using a double-split HMM.
Employed leave-one-out cross-validation for robust evaluation.
Explored generalization to dynamic Bayesian networks for heterogeneous data.

Conclusions:

Profile HMMs offer a significant improvement over existing methods for gene function prediction.
The developed tool demonstrates enhanced accuracy for unannotated genes.
Future work includes integrating diverse datasets using Bayesian networks.