Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Predicting gene expression from sequence: a reexamination.

Yuan Yuan1, Lei Guo, Lei Shen

  • 1Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.

Plos Computational Biology
|December 7, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same author

Phenotypic prediction of missense variants via deep contrastive learning.

Nature biomedical engineering·2026
Same author

DEDUCE: statistical inference on disease-associated genes uncovers tissue-disease associations.

NAR genomics and bioinformatics·2026
Same author

Designing strongly coupled polaritonic structures via statistical machine learning.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2025
Same author

Participation bias in the estimation of heritability and genetic correlation.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.

PLoS computational biology·2026
Same journal

Extracting host-specific developmental signatures from longitudinal microbiome data.

PLoS computational biology·2026
Same journal

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks.

PLoS computational biology·2026
Same journal

Predictive coding explains asymmetric connectivity in the brain: A neural network study.

PLoS computational biology·2026
Same journal

Zooplankton feeding behavioral signatures in the morphology of macroscale prey spatial distribution.

PLoS computational biology·2026
Same journal

A brief overview of 20 years of neuroscience in PLoS Computational Biology.

PLoS computational biology·2026
See all related articles

Researchers developed a simpler method using naïve Bayes classifiers to predict gene expression patterns in yeast. This approach achieved higher accuracy than previous complex models, suggesting simpler rules for gene regulation.

Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Deciphering gene expression from genomic information is challenging.
  • Previous methods like Beer and Tavazoie (BT) used complex models to predict mRNA expression from promoter sequences.
  • Understanding gene regulation is crucial for biological research.

Purpose of the Study:

  • To reexamine and simplify the approach for predicting mRNA expression patterns in Saccharomyces cerevisiae.
  • To compare the accuracy of a naïve Bayes classifier with BT's complex Bayesian network models.
  • To investigate the biological validity of BT's predictions regarding motif positions and orientations.

Main Methods:

  • Trained naïve Bayes classifiers using sequence-motif matching scores from BT.

Related Experiment Videos

  • Utilized cross-validation (CV) procedures for accuracy assessment.
  • Analyzed combinatorial rules for specific motifs (PAC and RRPE).

    • Main Results:

    • The naïve Bayes models achieved 79% accuracy in predicting gene expression patterns, outperforming BT's 73% accuracy.
    • The simpler models did not require position and orientation information of binding sites.
    • BT's predictions on motif orientations and positions were found to be circumstantial, with simpler, more significant rules identified.

    Conclusions:

    • A simplified approach using naïve Bayes classifiers can effectively predict gene expression patterns.
    • Complex Bayesian network models may not be necessary for accurate gene expression prediction.
    • The cross-validation procedure used by BT may have overestimated their method's accuracy.