Prediction of Contact Maps Using Support Vector Machines

Abstract

Contact map prediction is of great interests for its application in fold recognition and protein 3D structure determination. In particular, we focusd on predicting non-local interactions in this paper. We employed Support Vector Machines (SVMs) as the machine learning tool and incorporated AAindex to extract correlated mutation analysis (CMA) and sequence profile (SP) features. In addition, we evaluated the effectiveness of different features for various fold classes. On average, our predictor achieved an prediction accuracy of 0.2238 with an improvement over a randompredictor of a factor 11.7, which is better than reported studies. Our study showed that predicted secondary structure features play an important roles for the proteins containing beta structures. Models based on secondary structure features and CMA features produce different sets of predictions. Our study also suggests that models learned separately for different protein fold families may achieve better performance than a unified model.

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Document Details

Document Type
Technical Report
Publication Date
Dec 09, 2002
Accession Number
ADA439501

Entities

People

  • George Karypis
  • Ying Zhao

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Amino Acids
  • Computer Science
  • Computers
  • Data Science
  • Data Sets
  • Databases
  • Factor Analysis
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Network Science
  • Neural Networks
  • Supervised Machine Learning
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks