By Seon-Kyung Woo, Chang-Beom Park, Seong-Whan Lee (auth.), De-Shuang Huang, Xiao-Ping Zhang, Guang-Bin Huang (eds.)
The overseas convention on clever Computing (ICIC) was once organize as an annual discussion board devoted to rising and hard subject matters within the quite a few facets of advances in computational intelligence fields, corresponding to synthetic intelligence, desktop studying, bioinformatics, and computational biology, and so forth. The aim of this convention was once to compile researchers from academia and in addition to practitioners to proportion principles, difficulties and ideas relating to the multifaceted facets of clever computing. This e-book constitutes the court cases of the foreign convention on clever Computing (ICIC 2005), held in Hefei, Anhui, China, in the course of August 23–26, 2005. ICIC 2005 acquired over 2000 submissions from authors in 39 nations and areas. in keeping with rigorous peer reports, this system Committee chosen 563 high quality papers for presentation at ICIC 2005; of those, 215 papers have been released during this e-book prepared into nine different types, and the opposite 348 papers have been released in 5 overseas journals. The organizers of ICIC 2005 made nice efforts to make sure the luck of this convention. We right here thank the individuals of the ICIC 2005 Advisory Committee for his or her assistance and suggestion, the individuals of this system Committee and the referees for reviewing the papers, and the individuals of the ebook Committee for checking and compiling the papers. we might additionally prefer to thank the writer, Springer, for his or her aid in publishing the complaints within the Lecture Notes in machine technology sequence. rather, we want to thank the entire authors for contributing their papers.
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Additional resources for Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part II
Therefore we made binary SVM classifiers and assembled binary classifiers to predict the secondary structures of proteins according to the method proposed by Hua and Sun. We built three one-versus-all classifiers(H/~H, E/~E, C/~C) and three one-versus-one classifiers(H/E, E/C, C/H), and assembled these binary classifiers to make following five classifiers; SVM_TREE1, SVM_TREE2, SVM_TREE3, SVM_MAX_D and SVM_VOTE classifiers. SVM_TREE1, SVM_TREE2 and SVM_TREE3 classifiers were composed of one one-versus-all classifier and one one-versus-one classifier.
In Cyber-T, it combines information from gene-specific and global average variance estimates and thus should be more stable. But it will generate bias when the assumption of normal distribution is violated. 5) to determine the relative weights of gene-specific variance and global average variance, and reports its significance by permutation analysis. 14 W. Wang, Y. Zhou, and R. Bi Suppose that level of gene i in sample j (i = 1, 2,L , m; j = 1,L , n1, n1 + 1,L , n1 + n 2) , where the first n1 and the last n2 samples are obtained under two conditions, respectively.
Six subsets are used for training and one subset is used for testing. 2 Prediction Measurements Three standard measurements were used to assess the prediction accuracy of the proposed method. The Q3 accuracy indicates the percentage of correctly predicted residues of three secondary structures. The Q3 accuracy is the most obvious measurements for the overall accuracy of protein secondary structure prediction. 3 Combined Profile Generation We transformed RS126 data set into FASTA format to get evolutionary information using PSI-BLAST and RPS-BLAST.