Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data
- 26h 15m
- Albert Y. Zomaya (eds), Mourad Elloumi
- John Wiley & Sons (US)
- 2014
The first comprehensive overview of preprocessing, mining, and postprocessing of biological data
Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered.
Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems.
BIOLOGICAL DATA PREPROCESSING
- Part A: Biological Data Management
- Part B: Biological Data Modeling
- Part C: Biological Feature Extraction
- Part D Biological Feature Selection
BIOLOGICAL DATA MINING
- Part E: Regression Analysis of Biological Data
- Part F Biological Data Clustering
- Part G: Biological Data Classification
- Part H: Association Rules Learning from Biological Data
- Part I: Text Mining and Application to Biological Data
- Part J: High-Performance Computing for Biological Data Mining
Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.
About the Editors
MOURAD ELLOUMI is a Full Professor in Computer Science at the University of Tunis-El Manar, Tunisia. He is the author/coauthor of more than fifty publications in international journals and conference proceedings and the coeditor, along with Albert Zomaya, of Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications (Wiley).
ALBERT Y. ZOMAYA is the Chair Professor of High Performance Computing & Networking at The University of Sydney's School of Information Technologies. He is the author/coauthor of seven books, more than 450 publications in technical journals and conference proceedings, and the editor of fourteen books and nineteen conference volumes. He is a Fellow of the IEEE, the American Association for the Advancement of Science, and IET (UK).
In this Book
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Genome and Transcriptome Sequence Databases for Discovery, Storage, and Representation of Alternative Splicing Events
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Cleaning, Integrating, and Warehousing Genomic Data from Biomedical Resources
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Cleansing of Mass Spectrometry Data for Protein Identification and Quantification
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Filtering Protein–Protein Interactions by Integration of Ontology Data
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Complexity and Symmetries in DNA Sequences
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Ontology-Driven Formal Conceptual Data Modeling for Biological Data Analysis
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Biological Data Integration Using Network Models
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Network Modeling of Statistical Epistasis
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Graphical Models for Protein Function and Structure Prediction
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Algorithms and Data Structures for Next-Generation Sequences
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Algorithms for Next-Generation Sequencing Data
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Gene Regulatory Network Identification With Qualitative Probabilistic Networks
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Comparing, Ranking, and Filtering Motifs with Character Classes: Application to Biological Sequences Analysis
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Stability of Feature Selection Algorithms and Ensemble Feature Selection Methods in Bioinformatics
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Statistical Significance Assessment for Biological Feature Selection: Methods and Issues
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Survey of Novel Feature Selection Methods for Cancer Classification
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Information-Theoretic Gene Selection in Expression Data
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Feature Selection and Classification for Gene Expression Data Using Evolutionary Computation
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Building Valid Regression Models for Biological Data Using Stata and R
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Logistic Regression in Genomewide Association Analysis
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Semiparametric Regression Methods in Longitudinal Data: Applications to AIDS Clinical Trial Data
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The Three Steps of Clustering in the Post-Genomic Era
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Clustering Algorithms of Microarray Data
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Spread of Evaluation Measures for Microarray Clustering
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Survey on Biclustering of Gene Expression Data
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Multiobjective Biclustering of Gene Expression Data with Bioinspired Algorithms
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Coclustering Under Gene Ontology Derived Constraints for Pathway Identification
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Survey on Fingerprint Classification Methods for Biological Sequences
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Microarray Data Analysis: from Preparation to Classification
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Diversified Classifier Fusion Technique for Gene Expression Data
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Rna Classification and Structure Prediction: Algorithms and Case Studies
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AB Initio Protein Structure Prediction: Methods AND Challenges
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Overview of Classification Methods to Support HIV/AIDS Clinical Decision Making
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Mining Frequent Patterns and Association Rules from Biological Data
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Galois Closure Based Association Rule Mining From Biological Data
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Inference of Gene Regulatory Networks Based on Association rules
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Current Methodologies for Biomedical Named Entity Recognition
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Automated Annotation of Scientific Documents: Increasing Access to Biological Knowledge
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Augmenting Biological Text Mining with Symbolic Inference
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Web Content Mining for Learning Generic Relations and Their Associations from Textual Biological Data
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Protein–Protein Relation Extraction from Biomedical Abstracts
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Accelerating Pairwise Alignment Algorithms by Using Graphics Processor Units
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High-Performance Computing in High-Throughput Sequencing
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Large-Scale Clustering of Short Reads for Metagenomics on Gpus
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Integration of Metabolic Knowledge for Genome-Scale Metabolic Reconstruction
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Inferring and Postprocessing Huge Phylogenies
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Biological Knowledge Visualization
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Visualization of Biological Knowledge Based on Multimodal Biological Data
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