An Introduction to Quantum Stochastic Calculus / K. R. Parthasarathy

• Record Nr.: UNICAMPANIAVAN00289264
• Autore: Parthasarathy, Kalynapuram R.
• Pubbl/distr/stampa: Basel, : Birkhäuser, 1992
• Edizione: [Repr. of the 1992 ed]
• Descrizione fisica: xi, 290 p. : ill. ; 24 cm
• Soggetti: 60-XX - Probability theory and stochastic processes [MSC 2020]; 60Hxx - Stochastic analysis [MSC 2020]; 81-XX - Quantum theory [MSC 2020]; 81S25 - Quantum stochastic calculus [MSC 2020]
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Knowledge discovery in bioinformatics : techniques, methods, and applications / / edited by Xiaohua Hu, Yi Pan

• Record Nr.: UNINA9911020205003321
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, c2007
• ISBN: 9786610900183; 9781280900181; 1280900180; 9780470124642; 0470124644; 9780470124635; 0470124636
• Descrizione fisica: 1 online resource (405 p.)
• Collana: Wiley Series in Bioinformatics
• Altri autori (Persone): HuXiaohua <1960->; PanYi <1960->
• Disciplina: 570/.285
• Soggetti: Bioinformatics; Computational biology
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: 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 Analyses of Arabidopsis thaliana Tiling Array Expression Data; 4.1 Tiling Array Design and Data Description; 4.1.1 Data; 4.1.2 Tiling Array Expression Patterns; 4.1.3 Tiling Array Data Analysis; 4.2 Ontology Analyses; 4.3 Antisense Regulation Identification; 4.3.1 Antisense Silencing4.3.2 Antisense Regulation Identification; 4.4 Correlated Expression Between Two DNA Strands; 4.5 Identification of Nonprotein Coding mRNA; 4.6 Summary; Acknowledgments; References; 5 Identification of Marker Genes from High-Dimensional Microarray Data for Cancer Classification; 5.1 Feature Selection; 5.1.1 Taxonomy of Feature Selection; 5.1.2 Evaluation Criterion; 5.1.3 Generation Procedure; 5.2 Gene Selection; 5.2.1 Individual Gene Ranking; 5.2.2 Gene Subset Selection; 5.2.3 Summary of Gene Selection; 5.3 Comparative Study of Gene Selection Methods; 5.3.1 Microarray Data Descriptions5.3.2 Gene Selection Approaches; 5.3.3 Experimental Results; 5.4 Conclusions and Discussion; Acknowledgments; References; 6 Patient Survival Prediction from Gene Expression Data; 6.1 General Methods; 6.1.1 Kaplan-Meier Survival Analysis; 6.1.2 Cox Proportional-Hazards Regression; 6.2 Applications; 6.2.1 Diffuse Large-B-Cell Lymphoma; 6.2.2 Lung Adenocarcinoma; 6.2.3 Remarks; 6.3 Incorporating Data Mining Techniques to Survival Prediction; 6.3.1 Gene Selection by Statistical Properties; 6.3.2 Cancer Subtype Identification via Survival Information; 6.4 Selection of Extreme Patient Samples
• Sommario/riassunto: 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.