Article

Weave amino acid sequences for protein secondary structure prediction

Authors:

Bin WangAuthors Info & Claims

DMKD '03: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery

Pages 80 - 87

https://doi.org/10.1145/882082.882100

Published: 13 June 2003 Publication History

Abstract

Given a known protein sequence, predicting its secondary structure can help understand its three-dimensional (tertiary) structure, i.e., the folding. In this paper, we present an approach for predicting protein secondary structures. Different from the existing prediction methods, our approach proposes an encoding schema that weaves physio-chemical information in encoded vectors and a prediction framework that combines the context information with secondary structure segments. We employed Support Vector Machine (SVM) for training the CB513 and RS126 data sets, which are collections of protein secondary structure sequences, through sevenfold cross validation to uncover the structural differences of protein secondary structures. Hereafter, we apply the sliding window technique to test a set of protein sequences based on the group classification learned from the training set. Our approach achieves 77.8% segment overlap accuracy (SOV) and 75.2% three-state overall per-residue accuracy (Q₃), which outperform other prediction methods.

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Cited By

Morgado LPereira CVeríssimo PDourado A(2011)Peptidase Detection and Classification Using Enhanced Kernel Methods with Feature Selection5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011)10.1007/978-3-642-19914-1_4(23-30)Online publication date: 2011
https://doi.org/10.1007/978-3-642-19914-1_4
Kurgan LCios KChen K(2008)SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequencesBMC Bioinformatics10.1186/1471-2105-9-2269:1Online publication date: 1-May-2008
https://doi.org/10.1186/1471-2105-9-226
Kurgan LChen K(2007)Prediction of protein structural class for the twilight zone sequencesBiochemical and Biophysical Research Communications10.1016/j.bbrc.2007.03.164357:2(453-460)Online publication date: Jun-2007
https://doi.org/10.1016/j.bbrc.2007.03.164
Show More Cited By

Weave amino acid sequences for protein secondary structure prediction
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image ACM Conferences

DMKD '03: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery

June 2003

103 pages

ISBN:9781450374224

DOI:10.1145/882082

Conference Chairs:
Mohammed J. Zaki
Rensselaer Polytechnic Institute, Troy, New York
,
Charu C. Aggarwal
IBM T.J. Watson Research Center, Yorktown Heights, New York

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2003

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DMKD03: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery ( held in conjunction with MOD/PODS 2003 conference / co-located with FCRC 2003 Conference )

June 13, 2003

California, San Diego

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Cited By

Morgado LPereira CVeríssimo PDourado A(2011)Peptidase Detection and Classification Using Enhanced Kernel Methods with Feature Selection5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011)10.1007/978-3-642-19914-1_4(23-30)Online publication date: 2011
https://doi.org/10.1007/978-3-642-19914-1_4
Kurgan LCios KChen K(2008)SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequencesBMC Bioinformatics10.1186/1471-2105-9-2269:1Online publication date: 1-May-2008
https://doi.org/10.1186/1471-2105-9-226
Kurgan LChen K(2007)Prediction of protein structural class for the twilight zone sequencesBiochemical and Biophysical Research Communications10.1016/j.bbrc.2007.03.164357:2(453-460)Online publication date: Jun-2007
https://doi.org/10.1016/j.bbrc.2007.03.164
Bose SBrowne AKazemian HWhite K(2007)Use of Artificial Neural Networks and Effects of Amino Acid Encodings in the Membrane Protein Prediction ProblemProgress in Pattern Recognition10.1007/978-1-84628-945-3_4(37-46)Online publication date: 2007
https://doi.org/10.1007/978-1-84628-945-3_4
Homaeian LKurgan LRuan JCios KChen K(2007)Prediction of protein secondary structure content for the twilight zone sequencesProteins: Structure, Function, and Bioinformatics10.1002/prot.2152769:3(486-498)Online publication date: 10-Jul-2007
https://doi.org/10.1002/prot.21527
Kedarisetti KChen KKapoor AKurgan L(2006)Prediction of the Number of Helices for the Twilight Zone Proteins2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology10.1109/CIBCB.2006.330972(1-7)Online publication date: Sep-2006
https://doi.org/10.1109/CIBCB.2006.330972
Kurgan LHomaeian L(2005)Prediction of secondary protein structure content from primary sequence alone – a feature selection based approachProceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/11510888_33(334-345)Online publication date: 9-Jul-2005
https://dl.acm.org/doi/10.1007/11510888_33

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