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

Decomposing gene expression into cellular processes.

E Segal1, A Battle, D Koller

  • 1Computer Science Department, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2003
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

Predicted no effect concentrations of antifungals for wastewater management and agricultural use.

Frontiers in toxicology·2026
Same author

Prediction of small-for-gestational age and fetal growth restriction at routine ultrasound examination at 35-37 weeks' gestation.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology·2025
Same author

Refining the hospitalization rate: A mixed methods approach to differentiate primary COVID-19 from incidental cases.

Infection prevention in practice·2024
Same author

Environmental and bioclimatic factors influencing yeasts and molds distribution along European shores.

The Science of the total environment·2022
Same author

[Relevance of health geographic research for dermatology].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete·2021
Same author

Mycosands: Fungal diversity and abundance in beach sand and recreational waters - Relevance to human health.

The Science of the total environment·2021
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

This study introduces a new probabilistic model to discover cellular processes from gene expression data. The algorithm allows genes to participate in multiple processes, identifying real biological pathways in yeast.

Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Gene expression data analysis is crucial for understanding cellular functions.
  • Existing methods like clustering often assume exclusive gene-process relationships.
  • Cellular processes involve complex interactions where genes can participate in multiple pathways.

Purpose of the Study:

  • To develop a probabilistic model for identifying cellular processes from gene expression data.
  • To create an algorithm capable of discovering processes where genes can belong to multiple pathways.
  • To analyze yeast gene expression data to validate the model's ability to find biological processes.

Main Methods:

  • A probabilistic model was formulated where gene expression is a sum of process activities.

Related Experiment Videos

  • An iterative algorithm, based on the Expectation-Maximization (EM) algorithm, was developed for matrix decomposition.
  • The algorithm decomposes gene expression matrices into a specified number of underlying processes.

    • Main Results:

    • The proposed algorithm successfully decomposed yeast gene expression data.
    • The identified components corresponded to known or plausible biological processes.
    • The model demonstrated the ability to handle genes participating in multiple cellular processes.

    Conclusions:

    • The developed probabilistic model and algorithm effectively identify cellular processes from gene expression data.
    • This approach offers an advancement over traditional clustering by allowing multi-process gene participation.
    • The findings suggest the model's utility in uncovering complex biological pathways in genomic studies.