"Sequence and Genome Analysis: Methods and Applications II" is a continuation of our previous book, entitled "Sequence and Genome Analysis: Methods and Applications." Chapter 1 covers several aspects of protein structural motifs, including the procedures and approaches for detecting and associating protein structural motifs to functions, as well as the issues and limitations of the methodologies based on 3D motif for function prediction. Chapter 2 proposes a computationally efficient algorithm for identifying structural motifs. This algorithm considers the dynamic nature of the folding process, captures both local and non-local interactions between residues, and identifies structural motifs in a 3D space. Chapter 3 discusses the Watson-Crick finite automata. Watson-Crick automata are devices with two reading heads that can scan the two strands of a DNA molecule and decide whether or not the molecule is acceptable. They are essential in the field of DNA computing and have an important role in formal language and automata theory. Chapter 4 introduces logoBar, which is a means for annotating and displaying protein sequence logos. Protein sequence logos represent information content matrices of stretches of conserved DNA or protein sequences using a paradigm, in which the height of letters represents the information contribution of each residue in a sequence alignment. Chapter 5 presents an extraction and visualization method of anatomic structures from volumetric medical images, using a new level set method and hybrid rendering technique. The extraction of 3D objects and its visualization is one of the most important steps in the analysis of the preprocessed medical image data. Such data can help in performing diagnosis, treatment planning, and treatment delivery. Chapter 6 takes viral channel forming proteins as an example to outline computational strategies in developing structurally and functionally optimized models (with a minimum input of experimental results), with the aim of understanding the function of a particular protein. Chapter 7 studies hydrophobicity, a topic that has long been considered as one of the primary driving forces in the folding of proteins. This chapter focuses on statistical methods based on factor analysis and filtering to extract cluster-dependent average hydrophobicity profiles in an aligned family of proteins. Meanwhile, Chapter 8 addresses surface-based DNA computing by proposing an algorithm, which can solve a hard computational problem, namely, the Satisfiability problem. This problem is well-known for its exponential difficulty. Chapter 9 introduces a software architecture called GAMESSP2, which is used for protein secondary structure prediction. SSP2 is an abstract software architecture that follows the guidelines imposed by all information sources deemed relevant in implementing a successful secondary structure predictor. GAME stands for Generic Architecture Based on Multiple Expert, and is a tool for designing, implementing, and tuning secondary structure predictors. Finally, Chapter 10 describes an electrical model for fast DNA detection. This model can achieve a resolution of 0.01% for the capacitive shift, using a differential reading of the signals detected by two sensors, namely, a dummy sensor and an active sensor. Similar to Volume I, this volume is suitable for advanced undergraduate students and postgraduate students. It takes a practical approach rather than a conceptual approach. Moreover, it offers a truly reader-friendly way to get to the subject related to bioinformatics and biotechnology, making it the ideal resource for any student new to this subject, and providing a definitive guide to anyone in this vibrant and evolving discipline. This book is an invaluable companion for students, from their first encounter with the subject to more advanced studies, while the full-color artworks are designed to present the key concepts with simplicity, clarity, and consistency.

According to W3C, the Semantic Web is a "web of data" that enables machines to understand the meaning (semantics) of the data/information presented in the World Wide Web. It extends the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other, enabling automated agents to access the Web more intelligently and perform tasks on behalf of users. With more research about semantic web in research organizations and in industry, the Semantic Web is quickly emerging as a well-recognized and important area of computer science. This book, Introduction to the Semantic Web: Concepts, Technologies and Applications, provides a quick review and discussion on some key problems in the semantic web. Specifically, this book focuses on the following emerging areas/questions: how we can make use of the semantic web and its technologies to improve searching (Chapter 1 to Chapter 3), what is the relationship between web services and the semantic web (Chapter 4 and Chapter 5), how we can develop social semantic web via social tagging (Chapter 6), how we can improve work collaboration and resources allocation by using semantic web (Chapter 7 and Chapter 9), what are the major privacy concerns in the semantic web (Chapter 10 and Chapter 11), and how to semantic web via data mining, Semantic Web Constraint Language and Web Ontology Language (Chapter 12 to Chapter 14). This book is suitable for advanced undergraduate students and postgraduate students. It takes a practical approach rather than a conceptual approach. It offers a truly reader-friendly way to get to the subject related to the semantic web, making it the ideal resources for any student who is new to this subject and providing a definitive guide to anyone in this vibrant and evolving discipline. This book is an invaluable companion for students from their first encounter with the subject to more advanced studies, while the full-color artworks are designed to present the key concepts with simplicity, clarity and consistency.

A Practical Guide to Bioinformatics is suitable for advanced undergraduates and postgraduates. It takes a practical approach rather than conceptual approach and is an invaluable companion for students from their first encounter with the subject through to more advanced studies. The extensive and full-color artworks are designed to present the key concepts with simplicity, clarity and consistency. This book begins with discussing how to manage the data archives securely (Chapter 1). Data is one of the major and necessary components for bioinformatics analysis. This chapter gives us an overview of how we can protect our data. Among all kinds of bio-data, microarray data is arguably one of the most powerful and widely-use data. Chapter 2 to Chapter 4 discusses what we can do on top of microarray data. Besides microarray data analysis, network analysis is another very important research area. Chapter 5 to Chapter 7 extends the use of microarray data and focuses on network analysis in three different topics, namely, gene regulatory network, fuzzy gene network and metabolic network. Apart from analysis, annotating and browsing gene sequences is an area that we should never ignore. Chapter 8 to Chapter 9 discusses this important area. The rest of the book discusses some interesting and emerging topics in bioinformatics, which includes meiotic recombination hotspots (Chapter 10), primer and probe design for genome-wide DNA (Chapter 11), single nucleotide polymorphism analysis (Chapter 12), gene transcription regulation (Chapter 13), and protein-protein interaction network comparison (Chapter 14).

Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. A more general term of forecasting is prediction. Prediction plays a very important role in our daily life. Its scope is extremely wide: weather forecasting, stock market prediction, supply-demand prediction, population forecasting, traffic prediction, protein-protein interaction prediction, etc. In order to facilitate our discussion, we can only include some topics that are widely being discussed and can reflect the interest of most people. Specifically, this book presents how forecasting models are being used and why they are important in the following areas: supply-demand analysis (Chapter 1), systems performance (Chapter 2 - 3), bioinformatics (Chapter 4 - 5), financial markets (Chapter 6 - 10), electrical load analysis (Chapter 11 - 13) and some other emerging/interesting areas (Chapter 14 - 16). Inside, you will find the latest techniques used in academic and industry today, discover the importance of forecasting and prediction and learn how it's accomplished. And you'll develop the necessary skills to meet the increased demand for thoughtful and realistic forecasts. We especially put an emphasis on the practical uses of forecasting & prediction in this book. All chapters in this book have gone through a peer review, a selection and an editing process so as to guarantee their quality. This book can be served as a reference book for undergraduate or postgraduate courses.