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 classification artificial neural system.

C Wu1, G Whitson, J McLarty

  • 1Department of Computer Science, University of Texas, Tyler 75701.

Protein Science : a Publication of the Protein Society
|May 1, 1992
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

[Neural network based on modified simplex method and its application in studying forest self-thinning].

Ying yong sheng tai xue bao = The journal of applied ecology·2002
Same author

[Quantitative characteristics of rare and endangered Castanopsis kawakamii population].

Ying yong sheng tai xue bao = The journal of applied ecology·2002
Same author

Autotransplantation of heart for repair of left ventricular rupture after mitral valve replacement.

Transplantation proceedings·2001
Same author

Histone tails modulate nucleosome mobility and regulate ATP-dependent nucleosome sliding by NURF.

Proceedings of the National Academy of Sciences of the United States of America·2001
Same author

[The intracellular signaling pathway initiated by ginsenosides in hematopoietic cells].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2001
Same author

[An epidemiological study on the factors influencing length of hospitalization for inpatients of burns].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2001
Same journal

Macromolecular crowding inhibits degradation of alpha-synuclein amyloid fibrils induced by cathepsins and MMP9.

Protein science : a publication of the Protein Society·2026
Same journal

Sequence-encoded differences in the conformational ensembles of CITED transcriptional activation domains impact coactivator binding.

Protein science : a publication of the Protein Society·2026
Same journal

The phospholipid biosynthesis enzyme PlsB contains three distinct domains for membrane association, lysophosphatidic acid synthesis, and dimerization.

Protein science : a publication of the Protein Society·2026
Same journal

Structural basis of ligand selectivity in FAD/NAD(P)H-dependent dehydrogenases: insights from trypanothione reductase and type II NADH dehydrogenase.

Protein science : a publication of the Protein Society·2026
Same journal

Achieving protease substrate-specific inhibition by mAb dual functional selections.

Protein science : a publication of the Protein Society·2026
Same journal

How important are quantum mechanical effects in controlling biological functions: Enzymes, electron transfer and bird navigation.

Protein science : a publication of the Protein Society·2026
See all related articles

A new neural network method, Protein Classification Artificial Neural System (ProCANS), rapidly classifies protein superfamilies with 90% accuracy. This approach efficiently organizes protein databases and aids gene recognition in large sequencing projects.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence in Biology

Background:

  • Large-scale protein sequence databases present challenges for organization and analysis.
  • Efficient methods are needed for rapid classification of unknown proteins into superfamilies.
  • Existing database search methods can be computationally intensive.

Purpose of the Study:

  • To develop a novel neural network-based method for rapid protein superfamily classification.
  • To create an alternative to traditional large database search and organization techniques.
  • To enhance gene recognition capabilities in large-scale genomic projects.

Main Methods:

  • Implementation of the Protein Classification Artificial Neural System (ProCANS) on a Cray supercomputer.

Related Experiment Videos

  • Utilizing an n-gram hashing function for protein sequence encoding, similar to k-tuple methods.
  • Employing modular back-propagation networks trained on a subset of the Protein Identification Resource database (620 superfamilies).

    • Main Results:

    • Achieved 90% predictive accuracy after seven Cray CPU hours of training.
    • Demonstrated rapid classification speed of 0.1 Cray CPU seconds per sequence.
    • Projected classification time of under 1 CPU second for a full-scale system, with sustained low classification times for increased data.

    Conclusions:

    • ProCANS offers a fast and accurate method for protein superfamily classification.
    • The system's efficiency is valuable for organizing protein sequence databases and gene recognition.
    • The ProCANS software and neural database are portable, benefiting the wider genomics community.