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Training Set Reduction Methods for Protein Secondary Structure Prediction in Single-Sequence Condition
 

Summary: Training Set Reduction Methods for Protein Secondary Structure
Prediction in Single-Sequence Condition
Zafer Aydin, Yucel Altunbasak
School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA 30332-0250, USA
aydinz@ece.gatech.edu,
yucel@ece.gatech.edu
Isa Kemal Pakatci, Hakan Erdogan
Faculty of Natural Sciences and Engineering
Sabanci University
Tuzla Istanbul, 34956 Turkey
isakemal@su.sabanciuniv.edu,
haerdogan@sabanciuniv.edu
Abstract-- Orphan proteins are characterized by the lack of
significant sequence similarity to database proteins. To infer the
functional properties of the orphans, more elaborate techniques
that utilize structural information are required. In this regard,
the protein structure prediction gains considerable importance.
Secondary structure prediction algorithms designed for orphan

  

Source: Aydin, Zafer - Department of Genome Sciences, University of Washington at Seattle
Erdogan, Hakan - Faculty of Engineering and Natural Sciences, Sabanci University

 

Collections: Biology and Medicine; Energy Storage, Conversion and Utilization