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PROTEIN FOLD CLASSIFICATION BASED ON MACHINE LEARNING PARADIGM - A REVIEW
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Katarzyna Stąpor
Veröffentlicht/Copyright:
1. Januar 2012
Abstract
Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.
Received: 2011-06-08
Revised: 2011-09-24
Accepted: 2011-12-5
Published Online: 2012
Published in Print: 2012
©Jagiellonian University, Medical College, Kraków, Poland, 2012
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Schlagwörter für diesen Artikel
supervised learning algorithm;
classifier;
features;
protein fold recognition proteins
Artikel in diesem Heft
- User Response to the Simulation of a Virtual Patient with Cranial Nerve Injury
- Automatic Protein Abbreviations Discovery and Resolution from Full-Text Scientific Papers: The PRAISED Framework
- PROTEIN FOLD CLASSIFICATION BASED ON MACHINE LEARNING PARADIGM - A REVIEW
- A survey of FPGA implementations of Artificial Spiking Neurons Models
- Computer modelling of pentoxifylline release from a cylinder-shaped hydroxylapatite carrier
- HEXAPOD - SIX-LEGGED WALKING ROBOT CONTROLLED WITH TODA-RAYLEIGH LATTICE
- Realistic model of spine geometry in the human skeleton in the Vicon system
- Use and Usability of Health related E-services among the Senior Citizens
- New Approach to Prostate Diagnosis - Perfusion CT Images Analysis using "Life Belt" Method
- Research on the changes in voice quality caused by tonsillectomy
