Super Granular SVM Feature Elimination (Super GSVM-FE) Model for Protein Sequence Motif Informnation Extraction - researchr publication related

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Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert W. Harrison, Yi Pan. Super Granular SVM Feature Elimination (Super GSVM-FE) Model for Protein Sequence Motif Informnation Extraction. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007, Honolulu, Hawaii, USA, April 1-5, 2007, Part of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2007). pages 317-322, IEEE, 2007. [doi]

The following publications are possibly variants of this publication:

Efficient Super Granular SVM Feature Elimination (Super GSVM-FE) model for protein sequence motif information extractionBernard Chen, Stephen Pellicer, Phang C. Tai, Robert W. Harrison, Yi Pan. ijfipm, 1(1):8-25, 2008. [doi]

Super Granular Shrink-SVM Feature Elimination (Super GS-SVM-FE) Model for Protein Sequence Motif Information ExtractionBernard Chen, Stephen Pellicer, Phang C. Tai, Robert W. Harrison, Yi Pan. bibe 2007: 379-386 [doi]

Mining Positional Association Super-Rules on Fixed-Size Protein Sequence MotifsBernard Chen, Sinan Kockara. bibe 2009: 1-8 [doi]

Protein Sequence Motif Super-Rule-Tree (SRT) Structure Constructed by Hybrid Hierarchical K-Means Clustering AlgorithmBernard Chen, Jieyue He, Stephen Pellicer, Yi Pan. bibm 2008: 98-103 [doi]

Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM)Bernard Chen, Matthew Johnson. bmcbi, 10(S-11):15, 2009. [doi]