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Knowledge Discovery in Bioinformatics by Xiaohua Hu,Yi Pan Pdf
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.
Biological Knowledge Discovery Handbook by Mourad Elloumi,Albert Y. Zomaya Pdf
The first comprehensive overview of preprocessing, mining,and postprocessing of biological data Molecular biology is undergoing exponential growth in both thevolume and complexity of biological data—and knowledgediscovery offers the capacity to automate complex search and dataanalysis tasks. This book presents a vast overview of the mostrecent developments on techniques and approaches in the field ofbiological knowledge discovery and data mining (KDD)—providingin-depth fundamental and technical field information on the mostimportant topics encountered. Written by top experts, Biological Knowledge DiscoveryHandbook: Preprocessing, Mining, and Postprocessing of BiologicalData covers the three main phases of knowledge discovery (datapreprocessing, data processing—also known as datamining—and data postprocessing) and analyzes both verificationsystems 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 DataMining Combining sound theory with practical applications in molecularbiology, Biological Knowledge Discovery Handbook is idealfor courses in bioinformatics and biological KDD as well as forpractitioners and professional researchers in computer science,life science, and mathematics.
Knowledge Discovery in Proteomics by Igor Jurisica,Dennis Wigle Pdf
Multi-modal representations, the lack of complete and consistent domain theories, rapid evolution of domain knowledge, high dimensionality, and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated, we have reached a point where
Computational Knowledge Discovery for Bioinformatics Research by Li, Xiao-Li Pdf
"This book discusses the most significant research and latest practices in computational knowledge discovery approaches to bioinformatics in a cross-disciplinary manner that is useful for researchers, practitioners, academicians, mathematicians, statisticians, and computer scientists involved in the many facets of bioinformatics"--
Knowledge Discovery and Emergent Complexity in Bioinformatics by Karl Tuyls,Ronald Westra,Yvan Saeys,Ann Nowé Pdf
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics, KDECB 2006, held in Ghent, Belgium, in May 2006, in connection with the 15th Belgium-Netherlands Conference on Machine Learning. The 12 revised full papers cover various topics in the areas of knowledge discovery and emergent complexity research in bioinformatics.
Semantic Web by Christopher J. O. Baker,Kei-Hoi Cheung Pdf
This book introduces advanced semantic web technologies, illustrating their utility and highlighting their implementation in biological, medical, and clinical scenarios. It covers topics ranging from database, ontology, and visualization to semantic web services and workflows. The volume also details the factors impacting on the establishment of the semantic web in life science and the legal challenges that will impact on its proliferation.
Bioinformation Discovery by Pandjassarame Kangueane Pdf
This new edition continues to illustrate the power of biological data in knowledge discovery. It describes biological data types and representations with examples for creating a workflow in bioinformation discovery. The concepts in knowledge discovery from data are illustrated using line diagrams. The principles and concepts in knowledge discovery are used for the development of prediction models for simulations of biological reactions and events. Advanced topics in molecular evolution and cellular & molecular biology are addressed using bioinformation gleaned through discovery. Each chapter contains approximately 10 exercises for practice. This will help students to expand their problem solving skills in Bioinformation Discovery. In this new edition, there are three new chapters covering single nucleotide polymorphism, genes, proteins and disease, and protein functions driven by surface electrostatics.
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics by Andreas Holzinger,Igor Jurisica Pdf
One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Selected Topics in Post-genome Knowledge Discovery by Limsoon Wong,Louxin Zhang Pdf
The Institute for Mathematical Sciences at the National University of Singapore organized a program on OC Post-Genome Knowledge DiscoveryOCO from January to June 2002. The program focused on the computational and statistical analysis of sequences and genetics, and the mathematical modeling of complex biological interactions, which are critical to the accurate annotation of genomic sequences, the study of the interplay between genes and proteins, and the study of the genetic variability of species. As part of the program, tutorials for graduate students and newcomers to this transdisciplinary area of research were given by experts in these fields. This important volume collects the expanded notes of some of the tutorials that were given during the program. The topics include comparison and alignment of biological sequences, modeling and analysis of biological pathways, data mining and knowledge discovery from biological and clinical data. Contents: Dynamic Programming Strategies for Analyzing Biomolecular Sequences (K-M Chao); The Representation, Comparison, and Prediction of Protein Pathways (J Tillinghast et al.); Gene Network Inference and Biopathway Modeling (S Miyano); Data Mining Techniques (M J Zaki & L Wong). Readership: Graduate students and researchers in knowledge discovery, statistical learning, computer algorithms, computer simulations, molecular biology and genomics who are interested in bioinformatics."
Knowledge-Based Bioinformatics by Gil Alterovitz,Marco Ramoni Pdf
There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.
Hierarchical Feature Selection for Knowledge Discovery by Cen Wan Pdf
This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.
Knowledge Discovery in Life Science Literature by Eric G. Bremer,Jörg Hakenberg,Eui-Hong Sam Han,Daniel Berrar,Werner Dubitzky Pdf
This book constitutes the refereed proceedings of the International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, held in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 12 revised full papers presented together with two invited talks were carefully reviewed and selected for inclusion in the book. The papers cover all topics of knowledge discovery in life science data.
Knowledge Discovery and Emergent Complexity in Bioinformatics by Karl Tuyls,Ronald Westra,Yvan Saeys,Ann Nowé Pdf
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics, KDECB 2006, held in Ghent, Belgium, in May 2006, in connection with the 15th Belgium-Netherlands Conference on Machine Learning. The 12 revised full papers cover various topics in the areas of knowledge discovery and emergent complexity research in bioinformatics.