The Purpose Of This Edited Book Is To Bring Together The Ideas And Findings Of Data Mining Researchers And Bioinformaticians By Discussing Cutting-edge Research Topics Such As, Gene Expressions, Protein/rna Structure Prediction, Phylogenetics, Sequence And Structural Motifs, Genomics And Proteomics, Gene Findings, Drug Design, Rnai And Microrna Analysis, Text Mining In Bioinformatics, Modelling Of Biochemical Pathways, Biomedical Ontologies, System Biology And Pathways, And Biological Database Management. Knowledge Discovery In Bioinformatics; Contents; Contributors; Preface; 1 Current Methods For Protein Secondary-structure Prediction Based On Support Vector Machines; 1.1 Traditional Methods; 1.1.1 Statistical Approaches; 1.1.2 Machine Learning Approaches; 1.2 Support Vector Machine Method; 1.2.1 Introduction To Svm; 1.2.2 Encoding Profile; 1.2.3 Kernel Functions; 1.2.4 Tertiary Classifier Design; 1.2.5 Accuracy Measure Of Svm; 1.3 Performance Comparison Of Svm Methods; 1.4 Discussion And Conclusions; References; 2 Comparison Of Seven Methods For Mining Hidden Links 2.1 Analysis Of The Literature On Raynaud's Disease2.2 Related Work; 2.3 Methods; 2.3.1 Information Measures; 2.3.2 Ranking Methods; 2.3.3 Seven Methods; 2.4 Experiment Results And Analysis; 2.4.1 Data Set; 2.4.2 Chi-square, Chi-square Association Rule, And Mutual Information Link Abc Methods Compared; 2.4.3 Chi-square Abc Method: Semantic Check For Mining Implicit Connections; 2.4.4 Chi-square And Mutual Information Link Abc Methods; 2.5 Discussion And Conclusions; Acknowledgments; References; 3 Voting Scheme-based Evolutionary Kernel Machines For Drug Activity Comparisons 3.1 Granular Kernel And Kernel Tree Design3.1.1 Definitions; 3.1.2 Granular Kernel Properties; 3.2 Gktsess; 3.3 Evolutionary Voting Kernel Machines; 3.4 Simulations; 3.4.1 Data Set And Experimental Setup; 3.4.2 Experimental Results And Comparisons; 3.5 Conclusions And Future Work; Acknowledgments; References; 4 Bioinformatics Analyse