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

Constructive induction and protein tertiary structure prediction

T R Ioerger1, L Rendell, S Subramaniam

  • 1Department of Computer Science, Beckman Institute, University of Illinois, Urbana 61801, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1993
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

AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM.

Acta crystallographica. Section D, Structural biology·2026
Same author

Job insecurity and psychological wellbeing among junior doctors in Malaysia: A national cross-sectional study.

The Medical journal of Malaysia·2026
Same author

Cytological development, calcium dynamic and metabolite profile of stress-induced tobacco (Nicotiana tabacum L.) microspore under calcium chloride treatment.

Brazilian journal of biology = Revista brasleira de biologia·2025
Same author

Exploring the Utility of the Modified Hospitalized-Patient One-Year Mortality Risk Score to Trigger Referrals to Palliative Care for Inpatients With Cancer.

Cancer medicine·2024
Same author

Morpho-anatomical characterization and DNA barcoding of Artemesia vulgaris L.

Brazilian journal of biology = Revista brasleira de biologia·2024
Same author

Links between socio-demographic characteristics and body mass index to colorectal cancer in North Borneo, Malaysia: A case-control study.

The Medical journal of Malaysia·2023
Same journal

Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 2000). San Diego, California, USA. August 19-23, 2000.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2001
Same journal

Analysis of gene expression data with pathway scores.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
Same journal

Towards a complete map of the protein space based on a unified sequence and structure analysis of all known proteins.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
Same journal

Mining for putative regulatory elements in the yeast genome using gene expression data.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
Same journal

A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
Same journal

Sequence database search using jumping alignments.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
See all related articles

Machine learning can improve protein structure prediction by analyzing physical and chemical properties of amino acid sequences. This novel approach identifies structural relationships missed by traditional homology-based methods.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in structural biology

Background:

  • Current protein structure prediction relies on homologous proteins with known structures.
  • Limitations exist for proteins with similar structures but low sequence homology.

Purpose of the Study:

  • To explore machine learning applications for enhanced protein structure prediction.
  • To develop a novel approach overcoming limitations of sequence homology-based methods.

Main Methods:

  • Investigated two machine learning strategies for protein structure prediction.
  • Developed a constructive induction approach using physical and chemical properties of amino acids.
  • Combined knowledge and search to improve sequence representation for semantic similarity detection.

Related Experiment Videos

Main Results:

  • A straightforward machine learning approach did not significantly improve classification over alignment scores alone.
  • The novel constructive induction method learned improved representations of amino acid sequences.
  • Demonstrated potential for discovering new structural relationships among protein sequences.

Conclusions:

  • Machine learning, particularly constructive induction using physical and chemical properties, offers a promising avenue for protein structure prediction.
  • This approach can identify structural relationships missed by traditional methods.
  • Highlights the role of knowledge integration in machine learning for complex biological domains.