LEADER 05354nam 2200697Ia 450 001 9910453805503321 005 20200520144314.0 010 $a1-281-92440-7 010 $a9786611924409 010 $a981-277-266-9 035 $a(CKB)1000000000555515 035 $a(EBL)1679435 035 $a(OCoLC)879023420 035 $a(SSID)ssj0000301752 035 $a(PQKBManifestationID)12090539 035 $a(PQKBTitleCode)TC0000301752 035 $a(PQKBWorkID)10266026 035 $a(PQKB)11346691 035 $a(MiAaPQ)EBC1679435 035 $a(WSP)00006268 035 $a(Au-PeEL)EBL1679435 035 $a(CaPaEBR)ebr10699038 035 $a(CaONFJC)MIL192440 035 $a(EXLCZ)991000000000555515 100 $a20070423d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aLife science data mining$b[electronic resource] /$feditors, Stephen Wong, Chung-Sheng Li 210 $aSingapore ;$aHackensack, NJ $cWorld Scientific$dc2006 215 $a1 online resource (388 p.) 225 1 $aScience, engineering, and biology informatics ;$vv. 2. 300 $aDescription based upon print version of record. 311 $a981-270-065-X 311 $a981-270-064-1 320 $aIncludes bibliographical references and index. 327 $aCONTENTS ; Preface ; Chapter 1 Survey of Early Warning Systems for Environmental and Public Health Applications ; 1. Introduction ; 2. Disease Surveillance ; 3. Reference Architecture for Model Extraction ; 4. Problem Domain ; 5. Data Sources ; 6. Detection Methods 327 $a7. Summary and Conclusion References ; Chapter 2 Time-Lapse Cell Cycle Quantitative Data Analysis Using Gaussian Mixture Models ; 1. Introduction ; 2. Material and Feature Extraction ; 3. Problem Statement and Formulation ; 4. Classification Methods ; 5. Experimental Results 327 $a6. Conclusion Appendix A ; Appendix B ; References ; Chapter 3 Diversity and Accuracy of Data Mining Ensemble ; 1. Introduction ; 2. Ensemble and Diversity ; 3. Probability Analysis ; 4. Coincident Failure Diversity ; 5. Ensemble Accuracy ; 6. Construction of Effective Ensembles 327 $a7. An Application: Osteoporosis Classification Problem 8. Discussion and Conclusions ; References ; Chapter 4 Integrated Clustering for Microarray Data ; 1. Introduction ; 2. Related Work ; 3. Data Preprocessing ; 4. Integrated Clustering ; 5. Experimental Evaluation 327 $a6. Conclusions References ; Chapter 5 Complexity and Synchronization of EEG with Parametric Modeling ; 1. Introduction ; 2. TVAR Modeling ; 3. Complexity Measure ; 4. Synchronization Measure ; 5. Conclusions ; References 327 $aChapter 6 Bayesian Fusion of Syndromic Surveillance with Sensor Data for Disease Outbreak Classification 330 $aThis timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science. The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in th 410 0$aScience, engineering, and biology informatics ;$vv. 2. 606 $aComputational biology$xMethodology 606 $aBioinformatics 608 $aElectronic books. 615 0$aComputational biology$xMethodology. 615 0$aBioinformatics. 676 $a570.2856312 701 $aWong$b Stephen T. C$0871702 701 $aLi$b Chung-Sheng$f1962-$0943815 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453805503321 996 $aLife science data mining$92130513 997 $aUNINA