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

Protein subcellular localization prediction based on compartment-specific biological features.

Chia-Yu Su1, Allan Lo, Hua-Sheng Chiu

  • 1Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|March 21, 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

Asynchronous transitions from high-risk hepatoblastoma to carcinoma.

Journal of hepatology·2026
Same author

Pediatric acute myeloid leukemia tumor composition predicts patient outcomes at diagnosis and reveals mechanisms of resistance to chemotherapy.

Research square·2026
Same author

Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML.

bioRxiv : the preprint server for biology·2025
Same author

Single-cell multi-omics as a window into the non-coding transcriptome.

Hereditas·2025
Same author

Coordinated regulation by lncRNAs results in tight lncRNA-target couplings.

Cell genomics·2025
Same author

Reply to comment on "Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence on clinical data".

American journal of infection control·2025
Same journal

Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2014
Same journal

Proceedings of Computational Systems Bioinformatics 2008. August 26-29, 2008. Palo Alto, California, USA.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Graph wavelet alignment kernels for drug virtual screening.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Fast multisegment alignments for temporal expression profiles.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Knowledge representation and data mining for biological imaging.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
See all related articles

This study introduces an improved method for predicting protein subcellular localization in Gram-negative bacteria using support vector machine (SVM) classifiers. The new approach achieves higher accuracy, aiding in genome annotation and protein function prediction.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Microbial Genomics

Background:

  • Accurate prediction of protein subcellular localization is crucial for understanding cellular mechanisms, genome annotation, and identifying drug targets.
  • Existing methods for predicting protein localization in Gram-negative bacteria have limitations in accuracy and scope.

Purpose of the Study:

  • To develop a novel computational method for predicting protein subcellular localization in Gram-negative bacteria.
  • To improve prediction accuracy compared to state-of-the-art systems.
  • To enhance the understanding of protein function and facilitate drug discovery.

Main Methods:

  • Utilized ten one-versus-one support vector machine (SVM) classifiers, each trained on compartment-specific biological features.

Related Experiment Videos

  • Integrated predictions from binary classifiers using a combination of majority voting and a probabilistic method.
  • Employed feature selection guided by biological knowledge for improved classifier performance.

    • Main Results:

    • Achieved an overall prediction accuracy of 91.4% on a benchmark dataset, surpassing the state-of-the-art by 1.6%.
    • Demonstrated significant performance improvement through biologically informed feature selection.
    • Obtained a highly accurate prediction of 92.8% for proteins with dual localizations.

    Conclusions:

    • The proposed method offers a significant advancement in predicting protein subcellular localization in Gram-negative bacteria.
    • Biologically guided feature selection is effective in enhancing the performance of one-versus-one SVM classifiers.
    • The model's high accuracy for dual-localized proteins has implications for comprehensive proteome annotation.