Three Dimensional Representation of Amino Acid Characteristics

Abstract

Amino acid substitution matrices which shows the similarity scores between pairs of amino acids have been widely used in protein sequence alignments. These matrices are based on the Dayhoff model of evolutionary substitution rates. Using machine learning techniques we obtained three dimensional representations of these matrices while preserving most of the information obtained in the matrices. Vector representation of amino acids has many applications in pattern recognition.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409552

Entities

People

  • E. Alpaydin
  • O. U. Sezerman
  • R. Islamaj

Organizations

  • Boğaziçi University

Tags

DTIC Thesaurus Topics

  • Amino Acids
  • Chemical Properties
  • Coding
  • Computational Biology
  • Decoding
  • Eigenvalues
  • Eigenvectors
  • Engineering
  • Information Theory
  • Learning
  • Machine Learning
  • Neural Networks
  • Sequences
  • Symbols
  • Three Dimensional
  • Unsupervised Machine Learning
  • Vector Spaces

Readers

  • Linear Algebra
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms